{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据处理\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 数据归一化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 回归模型\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "# 模型评估\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# 绘图\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = './data/'\n",
    "dname = 'day.csv'\n",
    "data = pd.read_csv(dpath + dname)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.数据基本信息"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "移除作业不需要的y值，即casual 和 registered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 14)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['casual','registered'], axis = 1, inplace = True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "划分训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data.loc[data['yr'] == 0]\n",
    "data_test = data.loc[data['yr'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 14)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练集\n",
    "data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(366, 14)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集\n",
    "data_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据数目正常，2012年为闰年，为366条数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed   cnt  \n",
       "0           2  0.344167  0.363625  0.805833   0.160446   985  \n",
       "1           2  0.363478  0.353739  0.696087   0.248539   801  \n",
       "2           1  0.196364  0.189405  0.437273   0.248309  1349  \n",
       "3           1  0.200000  0.212122  0.590435   0.160296  1562  \n",
       "4           1  0.226957  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出数值型特征（包括temp，atemp，hum，和windspeed）都是已经处理过的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>3405.761644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105.510663</td>\n",
       "      <td>1.110946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>274.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>6043.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season     yr        mnth     holiday     weekday  \\\n",
       "count  365.000000  365.000000  365.0  365.000000  365.000000  365.000000   \n",
       "mean   183.000000    2.498630    0.0    6.526027    0.027397    3.008219   \n",
       "std    105.510663    1.110946    0.0    3.452584    0.163462    2.006155   \n",
       "min      1.000000    1.000000    0.0    1.000000    0.000000    0.000000   \n",
       "25%     92.000000    2.000000    0.0    4.000000    0.000000    1.000000   \n",
       "50%    183.000000    3.000000    0.0    7.000000    0.000000    3.000000   \n",
       "75%    274.000000    3.000000    0.0   10.000000    0.000000    5.000000   \n",
       "max    365.000000    4.000000    0.0   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     0.684932    1.421918    0.486665    0.466835    0.643665    0.191403   \n",
       "std      0.465181    0.571831    0.189596    0.168836    0.148744    0.076890   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.325000    0.321954    0.538333    0.135583   \n",
       "50%      1.000000    1.000000    0.479167    0.472846    0.647500    0.186900   \n",
       "75%      1.000000    2.000000    0.656667    0.612379    0.742083    0.235075   \n",
       "max      1.000000    3.000000    0.849167    0.840896    0.972500    0.507463   \n",
       "\n",
       "               cnt  \n",
       "count   365.000000  \n",
       "mean   3405.761644  \n",
       "std    1378.753666  \n",
       "min     431.000000  \n",
       "25%    2132.000000  \n",
       "50%    3740.000000  \n",
       "75%    4586.000000  \n",
       "max    6043.000000  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 365 entries, 0 to 364\n",
      "Data columns (total 14 columns):\n",
      "instant       365 non-null int64\n",
      "dteday        365 non-null object\n",
      "season        365 non-null int64\n",
      "yr            365 non-null int64\n",
      "mnth          365 non-null int64\n",
      "holiday       365 non-null int64\n",
      "weekday       365 non-null int64\n",
      "workingday    365 non-null int64\n",
      "weathersit    365 non-null int64\n",
      "temp          365 non-null float64\n",
      "atemp         365 non-null float64\n",
      "hum           365 non-null float64\n",
      "windspeed     365 non-null float64\n",
      "cnt           365 non-null int64\n",
      "dtypes: float64(4), int64(9), object(1)\n",
      "memory usage: 42.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据完整，无空值，dteday为object型，从常理考虑日期对当天骑行人数影响有限，除非是有特定的活动，如果相关性不高的话考虑将其移除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Holiday')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b743575c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.countplot(data_train.holiday.values)\n",
    "plt.title('Holiday')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假期所占的比例很少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Weekday')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b71421748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.countplot(data_train.weekday.values)\n",
    "plt.title('Weekday')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "周一至周日分布均匀，数据没有问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Working day')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1SdZu2rRplsqVpPlnLOcggAOqakOSXYELkvxw1A9W1SpgFcCyZcuqrwIlab4bywiiqjZ077cDXwb2A25LsjtA9377OGqTJA3MekAkeWqSnaaWgT8BrgHOA1Z03VYA5852bZKkh41jimk34MtJpo5/VlX9e5LLgTVJjgVuAQ4bQ22SpM6sB0RV/Qh4UaP9TuDA2a5HktQ2SZe5SpImiAEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUtPEBUSSg5LckGRdkuPHXY8kzVcTFRBJtgc+DRwMLAWOTLJ0vFVJ0vw0UQEB7Aesq6ofVdUvgM8Dy8dckyTNSzuMu4BpFgG3Dq2vB1423CHJSmBlt/qzJDfMUm3zwQLgjnEXMQny0RXjLkFb8mdzygl5PPbynFE6TVpAtP7ktcVK1Spg1eyUM78kWVtVy8ZdhzSdP5vjMWlTTOuBvYbW9wQ2jKkWSZrXJi0gLgeWJNknyZOAI4DzxlyTJM1LEzXFVFWbk7wT+A9ge+C0qrp2zGXNJ07daVL5szkGqaqZe0mS5p1Jm2KSJE0IA0KS1GRAyNubaGIlOS3J7UmuGXct85EBMc95exNNuNOBg8ZdxHxlQMjbm2hiVdXFwF3jrmO+MiDUur3JojHVImmCGBCa8fYmkuYnA0Le3kRSkwEhb28iqcmAmOeqajMwdXuT64E13t5EkyLJ2cClwG8kWZ/k2HHXNJ94qw1JUpMjCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1PR/yks7SA5mFdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b74343b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.countplot(data_train.workingday.values)\n",
    "plt.title('Working day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "工作日和非工作日的数据也比较正常"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Weather')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yW5LXDT2TJpfkSUm+lOS/ut/f3ww905A8BzGFkjwPeAi4oqqeM/Q8mlySxcDiqropyeHABuCsqrp94NE0gSQBnlxVDyU5FLgBeF1VfWHg0QbhHsQUqqp1wL1Dz6E9V1Vbq+qm7vaDwB3AscNOpUnVyEPd3UO7y4L9v2gDIfUkyRLgJOCLw06iPZHk4CQ3A9uBNVW1YH9/BkLqQZLDgKuBi6rqgaHn0eSq6pGqOpHRNzmcnGTBHuY1ENI+1h27vhr4SFV9fOh5tHeq6n7gM8DpA48yGAMh7UPdSc7LgDuq6l1Dz6M9k2QmyRHd7Z8Afhv46rBTDcdATKEkVwKfB56ZZHOSC4aeSRM7DTgPeEGSm7vLmUMPpYktBq5Lcguj74ZbU1WfGnimwfg2V0lSk3sQkqQmAyFJajIQkqQmAyFJajIQkqQmAyFJajIQkqSm/wNv6azKR2yYfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b713297f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.countplot(data_train.weathersit.values)\n",
    "plt.title('Weather')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好天气居多，没有大雨大雪等恶劣天气"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lXZ5XeMu6Xcc51wbUASMDbNtrm94ppzuAl4KoUSTsPPTGHvafOM23rptCXMzQ6S5cWJRNWVUDe481+F2KBCmYb2d3QzDO7InqaZ2zXd7VT4E/Oufe6rYos7vNbIOZbTh27Fh3q4iErKP1TTz8Zhk3zMxj/vmZfpczqN67t4ZGP4WPYIKiAug6sHsUcOa9Dd9fx8xigFSgOsC2Ads0s28BmcBXeyrKOfeIc26Oc25OZubQ+ocm4a3DOX636RBJ8TH8w7VFfpcz6EaNSKQoN4XVOxQU4SKYoFgPTDCzsWYWR2fn9Ioz1lkB3Ok9vgl43etjWAEs9UZFjQUm0Nnv0GObZvZZ4Crg4865jr7tnkjoWb+/moPVp/mHa4rIGB458zmdjSsmZ7HxQA01p1r8LkWC0GtQeH0O9wAvAzuAp5xz283sATO73lvtUWCkmZXReRRwr7ftduApoITOvoYvOefae2rTa+tnQDbwjpltNrP7+2lfRXxX19jKS9uOMD4ziY/NHrqT411RlE2HgzdKq/wuRYIQ1OyxzrlVwKozlt3f5XETcHMP234X+G4wbXrLNaOtRKyVxZW0dzhumJlP58DAoWlqXipZyfG8tqOKG2drYGOoGzpDLUR8VlJZx/bKehZMymLkED3l9J6oKGPB5Cz+sOsYLW06wxzqFBQig+B0SxsrtlSSkzKMSydo8AXAgknZNDS3sXafJgkMdTrNI77qcI7jDc1UN7S8Pz56RGIcmcnxYX+v6Pc453hucyUNzW3ccVFBxOxXX11SmEF8TBSv7ajiwwrPkKagkEHX4Ry7jp5k3b5q9h0/RXM3px6io4z8tASa29q5ZlouWSnhO73F5vJath6qY2FRNvkjEvwuJ2QkxEVzaWEGq3cc5VvXFQ3pPptQp6CQQbW9so4Xtx2h+lQLycNimDk6jVEjEslKjicqyujocJw41cKRukZ2VzXwT8+X8M8rS7huRh5f+kgh52cn+70LZ6XmVAsrtlRy3sjEIXdhXTAWTM7mtZ1V7DrawMSc8Pq7HUoUFDIo6htbWbGlkpLD9eSmDuPjc8dQlJvS7WmY0emJMDqNRcDcsSNYvr6cJ9Ye5LnNlVw3I4/7Fk8iLy30/2fe3NbOk+s6Jye8+YLRROl/zH9hweQs+B2s3nFUQRHCFBQy4MqrT/P4mgM0t7WzaEoOlxRmBH2evjArmW9eU8QXLy/k0bf38fO39vJqyRG+dHkhn79sfEjPkfSdlTs4VNvI7fPGkJ4U53c5gyrQ7L1nyk9LYPn6ckYkxoXUzL7yv0L3X5lEhOKKWn7+1l5io40vXl7I/PMzz6kzd0RSHF+7aiKv/d1lfHRSFj94dRc3PPQndhyuH4Cq++65zYd4fM0BPlyYQVFeqt/lhLRJOcmUV5+mobnN71KkBwoKGTDvHqhh2fpy8kck8IXLC8nuhw7pUSMS+eknLuCROy6g6mQz1//kbX7y+m7a2kNnLP7GAzX8/TPFzC1IZ+GUHL/LCXmTc1NwQOmRk36XIj1QUMiAKK6o5bfvVjA+M4nPXDKW4fH9e5Zz4ZQcXvnb+Syamsu/vbKLGx/+M7uP+v+L5sCJU3zu1xvITR3Gz+64QENhg5CbOozUhFh2HgnNo0NRUMgAKD1ykqc2lHPeyETuuKiA2OiB+ZqlJ8Xx44/P4qHbZlNR08g1P36bR9/e59v9mI83NPPp/15Ph3P86lMXDrl+iXNlZkzMSWZ3VQPNbe1+lyPdUFBIv6qqb2LZ+oNkpwzjkxcXDEpn8zXTc3n5K/OZPyGDf15Zwu2PrqX29ODOSnq8oZlP/HwtlbWNPHLHHMZlDh/U9w93k3OSaWnrYM3ear9LkW4oKKTf1J1u5fE1B4iJjuKOi85jWGz0oL13ZnI8P//kHL534zS2lNfyH6/tZtPBGjpnux9Y74XEgepT/PLOC5k7Nn3A3zPSjMscTmy08ZruURGSNDxW+kVHh+P/LNtE7elWPvvhsaQl9s9pl7MZZvmeL1xeyNMbynl6YwXbKuu5fkYeqQmx/VLPmXYdPcldj62nqr6ZX37qQj5UmDEg7xPpYqOjKMxK5rUdVfzT9U5XaYcYHVFIv/j5W3v5465jXDsjl/NGJvlaS3pSHJ+bP45FU3LYffQk/756F3/ec5z2fu67eH3nUW786Z9pbOngN3dfxCUKiT6ZnJPModpGdmr0U8jREYX02ZbyWr7/cimLp+YwtyA0TrtEmTH//Eym5KXw3JZKVhYfZu2+ahZPycG5vv2Ptb6plX9ZtYPfrCunKDeFX9w5JyyuFA91712Z/dqOo0zOTfG5GulKRxTSJw3NbXx52SaykuP53o3TQ+6Uwcjh8Xz6QwXcPu88nHP8es0BbnjoT6wsrjzray+a29r5zbqDLPzhH1m+vpzPzx/Hs1/8kEKinyQPi2XG6DRW79Bd70KNjiikT7734g4OVp9m+d0Xk5o4MP0AfWVmFOWlMDEnmY0HathSUcs9T24iJ2UYi6flcPW0XKblp3bb+e6cY3tlPat3HOU36w5ytL6Z6aNS+dkdFzBzdJoPexPZrpiUxQ9X7+LYyWYyk4f2zZ1CiYJCztmfy47zP2sO8rkPjw2LkT7RUcbcsen84JYZvFpylN++W8ETaw7yqz/tJybKOD87mby0YSTExdDhHJW1jRw4cZrqUy2YwcXjRvJvN8/g0sKMkDtyihQLJmfzg1d38cbOKm65cLTf5YhHQSHn5FRzG1//bTFjM5L4u4UT/S7nrERHGYum5rBoag71Ta38afdxth6qY1tlPZW1TTS2tuOcIy8tgSsmZzF37Egun5hJxhC/felgmJybTH5aAq/uOKqgCCEKCulWoGGpt80bw7++tJNDtY089fmLB/V6if6WMiyWxdNyWTwt95zbOJchvH3ZLpL9Zl05o9MTebO0iv/+0/4PXLCpmWX9o85sOWuby2v59ZoDfPKi87gwREY5SeQoyk2htd1RVqVhsqFCQSFnpb3D8X+f3UpWcjxfuyq8TjlJeBibkURCbDTbKzVJYKhQUMhZeWfvCUoO1/Ot66aQPCw0RzlJeIuOMiblJLPzyMl+v0hSzo2CQoJW39jK6pKjfHRSFoun6j4LMnCK8lJobG1n3/FTfpciKCjkLLy8/QjtzvHt66ZoeKgMqAlZycRGGyWH6/wuRVBQSJDKq0+zqbyWSwszGDMy0e9yJMLFxUQxISuZksp6OgZhBmAJTEEhvepwjpXFlSQPi+Hy8zP9LkeGiKK8FOqb2jhU0+h3KUOegkJ6taW8lvKaRq6akkN8GF8zIeFlck4K0WZsq9TpJ78pKCSg5rZ2Xtp+hFEjEjS3kQyqhLhoxmclsfVQ3aDcgEp6pqCQgP5QeoyTTW1cOz2PKHVgyyCblp9K7elWDtXq9JOfFBTSo+pTLbxddpxZo9MYk64ObBl8k3NTiDLYekinn/ykoJAevbjtMFFmLJyiaybEH4lxMRRmDWebTj/5SkEh3dpzrIHtlfVcPjFzwO43LRKMqXmp1JxupbhCRxV+0eyx8hfa2jt4ofgwIxJjh9x9oHubNVcGX1FeCr/ffIgXth5mhgZU+EJHFPIXfrO+nCP1TSyemktstL4i4q/EuBgmZCWzckslHZr7yRf6LSAfUHu6hR++Usq4jCSm5OkG9xIaZoxOpbKuiXX7q/0uZUhSUMgH/Pvq3dQ1tnLN9FzN5yQhY3JuCgmx0Ty3+ZDfpQxJQQWFmS0ys1IzKzOze7t5Pd7MlnuvrzWzgi6v3ectLzWzq3pr08zu8ZY5MxtaJ8h9tvvoSR5fc4Db5o0hNzXB73JE3hcfE83CKdm8UHyY5rZ2v8sZcnoNCjOLBh4CFgNFwMfNrOiM1e4CapxzhcCPgAe9bYuApcAUYBHwUzOL7qXNPwFXAAf6uG9yFpxzPLCyhKS4aL56pW5IJKHnhpn51De18WbpMb9LGXKCGfU0Fyhzzu0FMLNlwBKgpMs6S4Bve4+fAX5inectlgDLnHPNwD4zK/Pao6c2nXObvGV92S/xBDuK57UdVby1+zjfuq6I9KS4c24zkg3V/Q4Vl07IID0pjuc2H+IqXdszqII59ZQPlHd5XuEt63Yd51wbUAeMDLBtMG3KIGlua+c7L5RQmDWc2y86z+9yRLoVGx3FtdNzWb2jirrGVr/LGVKCCYru/mt/5hi1ntY52+VBM7O7zWyDmW04dkyHon3x33/az/4Tp/nHa4s0HFZC2sdmj6KlrYPnt1T6XcqQEsxvhQpgdJfno4Az/5beX8fMYoBUoDrAtsG0GZBz7hHn3Bzn3JzMTN0j4VxVnWzix6+XccXkLC7TvSYkxE0flcqknGSe3lDe+8rSb4IJivXABDMba2ZxdHZOrzhjnRXAnd7jm4DXXefELCuApd6oqLHABGBdkG3KIPi3l0tpbmvnm9ecOT5BJPSYGbfMGc2Wijp2Hqn3u5who9eg8Poc7gFeBnYATznntpvZA2Z2vbfao8BIr7P6q8C93rbbgafo7Ph+CfiSc669pzYBzOzLZlZB51FGsZn9ov92V7raXF7L0xsr+PQlYxmbkeR3OSJBuWFWPrHRxlPrK/wuZcgIaq4n59wqYNUZy+7v8rgJuLmHbb8LfDeYNr3l/wn8ZzB1ybnrcI77n9tG5vB4vrxggt/liAQtPSmOhUU5/G5TBd9YPJH4GN11caBpUsAhasP+Goor6viPpTMZHj+0vgYa5hr+brlwNC9sPczqkiqumZ7rdzkRT0NchqDTzW28vP0I88amc/2MPL/LETlrlxZmkJ+WwONr9vtdypCgoBiCXik5SnNbOw8smaoLGyUsRUcZd1x8Hmv2VlN65KTf5UQ8BcUQU1FzmvX7q7l43Egm5iT7XY7IObt1zmjiY6L49Tv7/S4l4ikohpAO53h+SyVJ8TEsmJztdzkifTIiKY7rZ+Tx7LuHdKX2AFNQDCHvHqihvKaRxVNzGBarkSIS/u78UAGNre38dqOGyg4kBcUQ0dDcxovbjnDeyERm6naSEiGm5qcye0waj72zn3bd/W7ADK1xkUPYyuJKWto7+KuZ+e93YGuYqESCz314HF944l1e3HaYa6drFN9A0BHFELDzcD3FFXV8ZGImWSnD/C5HpF8tnJLDuIwkHn5zD50zB0l/U1BEuKbWdp7bUkl2SjzzNemfRKDoKOPzl41je2U9b+0+7nc5EUlBEeFe3n6E+sZWbpw1ipgo/XVLZLphVj7ZKfE8/OYev0uJSPrNEcHW7atm7b5qPjR+JKPTE/0uR2TAxMdE89lLx/HO3hNsPFDtdzkRR0ERoZpa27n3t8WMSIzlyiLdNlIi323zxpAxPI7vv1yqvop+pqCIUP/x2m72Hj/FDTPziYvRX7NEvqT4GL70kULW7K3m7TL1VfQn/QaJQBv2V/Nff9jDLXNGMSFb03TI0HHbvDHkpyXoqKKfKSgiTENzG199agv5IxK4/7opfpcjMqjiY6L5yhUTKK6o46VtR/wuJ2IoKCLMPz9fQkXNaX50y9C7z4QIwI2zRzEhazj/8uJOmlrb/S4nIigoIsjzWypZvqGcv75sPHMK0v0uR8QX0VHGt6+fwsHq0zzyx71+lxMRFBQR4sCJU9z37FZmj0njb6883+9yRHx1SWEG10zL5aE3yiivPu13OWFPQREBmtvauefJTURHGf/58VnERuuvVeSb10wmyowHVpb4XUrY02+UCPBPz5ew9VAd379pOqNG6MI6EYC8tAS+vGACr5YcZWVxpd/lhDX1doa536w7yJNrDzJ/QibHG1o0I6xErJ6+27fNG9PjNsPjYxg1IoG/f7qYippGUobFBrWdfJCOKMLYxgM13P/cNiZkDWfhFN2xTuRM0VHGTReMorW9g9+9e0jXVpwjBUWYKq8+zecf30huagK3XjiaKO8eEyLyQVnJw1g0NYfSoydZu0/zQJ0LBUUYqjvdyqd+tY6WtnZ++ak5JMbpDKJIIBeNG8nE7GReKD7MwROn/C4n7CgowkxTazufe3wD5dWN/PyTcyjM0hQdIr2JMuOWOaNJTYzliXUHqW9q9buksKKgCCMtbR188Yl3Wbevmu/fPJ1540b6XZJI2EiIi+b2eefR1NrOE2sO0Niiq7aDpaAIE20RLadHAAAKlElEQVTtHXxl+SZe31nFd26YypKZ+X6XJBJ2clKHcfMFo6moaeQLT2yktb3D75LCgoIiDLS0dfA3yzezausR/vHaIm6/6Dy/SxIJW1PzU7lhZj5vlh7ja09voaNDI6F6o17QEHe6pY0v/M+7/GHXMb559WTuunSs3yWJhL0Lx6YzIWc4//pSKR0OfnDzDN23JQAFRQg73tDM3b/ewObyWh782DRuvVAXCIn0ly9eXkiUGd97cSf1ja08fPtsjSDsgSI0RG2vrOP6H79NyeF6fvqJ2QoJkQHw15eN53s3TuOt3ce45b/e0QSCPVBQhBjnHE9vKOemh9/BAc/89YdYNDXX77JEItbSuWP4+SfncODEaa77ydv8Ydcxv0sKOQqKEFJ3upV7ntzE3z9TzIzRqay451Km5qf6XZZIxFswOZvn77mUnJRh3PnLdfzj77dxqrnN77JChk7IhQDnHCu2VPKdF3ZQc6qFry+ayOfnjyc6StNyiAyWgowkfvfFS/j+y6X86s/7eH1nFfdfV8TComxsiE+Ro6DwWXFFLf+yaifv7D3B9FGp/OpTF+ooQuQs9OeMyQlx0dx/XRFXT8vh3me38vnHNzK3IJ1vLJ7IBecN3btGKih8sqW8lofeKOOVkqOkJcbyzzdM5ba5Y3QUIRIC5hSk89LffJhl68v599W7+NjD7zC3IJ2754/jI5Oyhty/UwXFIGpobuPlbUd4fM0BNpfXkhwfw99ecT6fubSA5C7z5IuI/2Kio7j9ovP4q1n5LF9fzqNv7+Ozv95ATsowbpydz/Uz85iYnTwkTksFFRRmtgj4DyAa+IVz7ntnvB4P/Bq4ADgB3Oqc2++9dh9wF9AOfNk593KgNs1sLLAMSAfeBe5wzrX0bTf9U1XfxFu7j/PazqO8tqOK5rYOxmUk8e3rivjYBaMUECIhLik+hs9cOpY7Lj6P1SVHeXpjBT/7wx5++uYexqQnsmByFh8an8HcgnRSEyPz33OvQWFm0cBDwJVABbDezFY457reiPYuoMY5V2hmS4EHgVvNrAhYCkwB8oDVZna+t01PbT4I/Mg5t8zMfua1/XB/7OxAcs5xtL6ZfcdPsedYA1vKa9lUXktZVQMAGcPjufXC0Vw/I4/ZY0YQNcQOXUXCXWx0FIun5bJ4Wi5VJ5tYXVLFKyVHeGLtQX71p/2YQcHIJCbnJjMpJ4XJuSlMzE4mN21Y2N/HPpgjirlAmXNuL4CZLQOWAF2DYgnwbe/xM8BPrPN4bAmwzDnXDOwzszKvPbpr08x2AB8FbvPWecxrd0CCoqPD0e4c7R2dP20d//u4vcPR2t5BY2s7p1vaaWxpp7G1jfrGNk6caqHmVAsnTrVQfaqZ8upG9p84xekus1GOSIxl1pgR3Dg7n/kTMinKTVE4iESIrORh3DZvDLfNG0NTazuby2tZu7eaksN1bK+sZ9XWI++vawaZw+PJTUsgN2UY2SnxpCbEkuL9pCbEMjw+hriYKOJjooiPie7yOIrYmCiizIiyzunSzcA44/kAn/4KJijygfIuzyuAeT2t45xrM7M6YKS3fM0Z27437Wl3bY4Eap1zbd2s3+8+89h63iw9t4troqOMEYmxpCfFkZ+WwLxx6YzLSKIgI4mxGUnkpyUMiXOXIkPdsNhoLho3kou6TPvf0NxG6ZGT7D56ksq6Jg7XNnKkvondVSf5857jnGxuo7/uyrr6q/MH/L40wQRFd7/tztzFntbpaXl3x2GB1v/LoszuBu72njaYWWl364WRDOC430WEOH1GvdNn1LsM4Pgn/K6in0x4sE+bBzUVdTBBUQGM7vJ8FFDZwzoVZhYDpALVvWzb3fLjQJqZxXhHFd29FwDOuUeAR4KoPyyY2Qbn3By/6whl+ox6p8+od/qMzl4wPSzrgQlmNtbM4ujsnF5xxjorgDu9xzcBrzvnnLd8qZnFe6OZJgDremrT2+YNrw28Np87990TEZG+6vWIwutzuAd4mc6hrL90zm03sweADc65FcCjwONeZ3U1nb/48dZ7is6O7zbgS865doDu2vTe8hvAMjP7DrDJa1tERHxirr96VKRPzOxu73Sa9ECfUe/0GfVOn9HZU1CIiEhA4X0ViIiIDDgFhc/MbJGZlZpZmZnd63c9g8nMRpvZG2a2w8y2m9nfeMvTzexVM9vt/TnCW25m9p/eZ1VsZrO7tHWnt/5uM7uzp/cMV2YWbWabzGyl93ysma319ne5NygEb+DIcu8zWmtmBV3auM9bXmpmV/mzJwPHzNLM7Bkz2+l9py7Wd6mfOOf049MPnR35e4BxQBywBSjyu65B3P9cYLb3OBnYBRQB/wrc6y2/F3jQe3w18CKd19tcBKz1lqcDe70/R3iPR/i9f/38WX0VeBJY6T1/CljqPf4Z8AXv8ReBn3mPlwLLvcdF3vcrHhjrfe+i/d6vfv6MHgM+6z2OA9L0XeqfHx1R+Ov96VFc58SH702PMiQ45w475971Hp8EdtB5Jf4SOv/R4/15g/d4CfBr12kNndfc5AJXAa8656qdczXAq8CiQdyVAWVmo4BrgF94z43OqW6e8VY58zN677N7Blhw5nQ6zrl9QNfpdMKemaUA8/FGSTrnWpxztei71C8UFP7qbnqUAZuyJJR5p0hmAWuBbOfcYegMEyDLW62nzyvSP8d/B74OdHjPA01184HpdICu0+lE8mc0DjgG/Mo7RfcLM0tC36V+oaDwV9BTlkQyMxsO/Bb4inOuPtCq3Sw7q6lfwo2ZXQtUOec2dl3czaqul9ci9jPyxACzgYedc7OAU3SeaurJUP2czomCwl/BTI8S0cwsls6QeMI596y3+Kh3GgDvzypveU+fVyR/jpcA15vZfjpPTX6UziOMNG+6HPjg/r7/WZzFdDqRoAKocM6t9Z4/Q2dw6LvUDxQU/gpmepSI5Z07fxTY4Zz7YZeXuk4J03UalxXAJ70RKxcBdd7phJeBhWY2whvVstBbFvacc/c550Y55wro/H687pz7BD1PdXO20+lEBOfcEaDczCZ6ixbQOSOEvkv9we/e9KH+Q+foi110jkL5pt/1DPK+X0rnYX0xsNn7uZrOc+qvAbu9P9O99Y3OG17tAbYCc7q09Rk6O2jLgE/7vW8D9Hldzv+OehpH5y/6MuBpIN5bPsx7Xua9Pq7L9t/0PrtSYLHf+zMAn89MYIP3ffo9naOW9F3qhx9dmS0iIgHp1JOIiASkoBARkYAUFCIiEpCCQkREAlJQiIhIQAoKkUFkZgVmdpvfdYicDQWFyOAqABQUElZ0HYVIPzCzTwJf438vIGwH6oE5QA7wdefcM2a2BpgM7AMec879yKeSRYKmoBDpIzObAjwLXOKcO25m6cAPgSTgVmASsMI5V2hmlwNfc85d61vBImdJp55E+u6jwDPOueMAzrlqb/nvnXMdzrkSINu36kT6SEEh0ndG91NRN5+xjkhYUlCI9N1rwC1mNhI67/kdYN2TdN72VSRsxPS+iogE4pzbbmbfBf5gZu3ApgCrFwNtZrYF+G91Zks4UGe2iIgEpFNPIiISkIJCREQCUlCIiEhACgoREQlIQSEiIgEpKEREJCAFhYiIBKSgEBGRgP4/0nokDEfpC2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b76d44b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data_train.cnt.values, bins = 30, kde = True)\n",
    "plt.xlabel('cnt')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'cnt')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mp7jVXCU1Rp65JESgVYlyqnudVVJYVLn327dPp0Cy6qMoICkA0TDT9gCgWVtsUX3aevGLbP5ZCqgss2nWyNiQ/UNDUesK0TrmozpC99Chbp6VeUIKQPRC6MjOOj2AOgKkj+5+VYUXMuJ369bqx00Lxyadn0VO66aFctFYiXmy2zdJqALYghANsrwMx47B3r1gFn1+7GNw880byyaT7P3N4OjRzeWrq3DwIKytRa/+2lr0fXW1en2OHYvK22B1NTpPFZ5+er2OeTz/fPW67NlT/L2sPI/4vwg977ScO5f/W9azIioQoiX6WtQDmC2qtNDzwj3zMmnOSuRGkfknxFRTd26CaXwAVXtDTTitq7Bly3Q9ja7DTIcAMgGJriiyT4dEk8B6Nz/vZZyV2O0yh26ZAK7qQymafCWLJgRf1fkcpmFlJV8BpAcRxtuXRTm1PdBsCEgBiE6YNoY9tFU66z2AuJ7TjBrOavnn5Slqs3Xb5X9RphDLUldX6VEN7VmaBikA0QkhuWHq7D+ZTB+v3gdNmFmy8gtlpYAIdbg3fZ/KrrFJBRQiwEPGU4QsQ+tNToMUgOiEurlhQvdPCpi8ePUh2XPT5rC6cwWHXFMVgdd06zavfk1HXYVcY+j0leoBSAGIhgkdaVt1hGjoCzqkUZ1d16WvVMpFNG0eChlUFx97mqkxh9ibnAYpANEJoaNem5jkPEuIDcU3sLLS/YTlffYA8mjDWZ8MFshLvb2yUp5zKm+p20sbMo0rAGAB+HPgdv/9JcA9wEngE8CSLz/Pfz/lf9+XOMZ7fPkJ4A1l55QCmA2K5qQNEUJFg4rK9m96cFNTKRG6aH335QMoom2FnPcfTWP/nyfTT0wbCuDdwH9LKIDbgGv8+o3AIb9+GLjRr18DfMKvvwz4olcQLwEeBhaKzikFMBuEvnxFgrBKtEfIflu2VBN605hvyurepoAZWox7Xya5aez/8+T8jWlUAQCXAncDPwrcDhjwFLDV//4q4E6/fifwKr++1W9nvvX/nsQxv7Nd3iIFMHxCW+9lgrCoFV3URS/q+qcniS9impZrkfCZN9tyCH0ooC57AEMKOsijaQXwKeAVwGu9ArgYOJX4/TLgQb/+IHBp4reH/fYfBN6aKL8JeHPGuQ4Cx4Hje/bs6eRmiXpUsd+HCMK6A8pCkqmVvbDTmJLyhE96yskx0bWQrDJ+YhoFPaSggyIaUwDATwA3+PVYAezKUABf8usPZSiACfChDAXwU0XnVg9g2JS1uuIRnFUEQJ2WeEjK57IXdpoewKwIha7o4n5kKZgQgT/tlJpDCTooo0kF8B+Bx4BHgb8GzgKrMgGJIsFbN8d8nTQDRYooNDJnWqE1RLNAX3XqwhGc9V+VhYHW7ZEl72PesYfmR2jcCRwdM+oB+PVPppzAh/36O1NO4Nv8+vennMBfkRN4tikaxduGQ7UolDTLD1CUgjpvHuGuBWbynKGza4Uet69eSdt5m6o8d9OeP9TMObc9gA0bb1QAlwP3EoV1fhI4z5e/yH8/5X+/PLH/EW8SOgFcVXY+KYBhU7UlVtecEnKMvBG4Q+6yl11rnekgYyXSp6Bq+54XKZimx2OEOpeHNpagFQXQ9SIFMHyyWs3TtgCL7LlVW3FFLeG+TCRFiqlIaOWFfIY64ptshZddX5u9jzIF0+T5qziWh+T3kQIQrVEmOJtoATbZigwVnF28wHUFdl27dx89gLi+bSnXrHuRnkuiqfMXRXjVVdhdNDqkAEQrhAjOJoRr363Itqgarx7XZ5o49yG2UKcla/R5HZNZmRDOUzZ1FDZsntqzrf9ECkC0QqjgbKKl02Zrqa8JZuqaFJrIdDkvwt+5+gq8TsMiabILzX7bd54mKQDRCrMyM1cZQ+wBFEUB1Y18abKFOaRQ1yJBXFS3ovtYdm1V0pX0nalVCkC0wpCjaqowJB9A6CjpEGd2k6GkIeevO96j6DwhxwsRxll1qzL/RPrcVZSOegBSAHPJPI16HUIUUNlcyFn79dECzxNode3wWVR5tkKc6Vl1q+I0TwvmvH0nk/U6JRVxXUXTBFIAojWGZAqYVUKd6UO5z9POrRtyLVV7l1XCaZPCus78E3mDDSEacJg1ZWnWZPZbtrTTQ0sjBSDEgOkylj0+XhVlkt6+Sss5bdMOvZa6/qUqSiAeLBZybUnFU3aOvLDQLGXRhSKXAhBiwJQJu6bHQVRRJlnbZ000k3cN6TqGXkuTkT2hdSvaPzmuYNoorGn/w6pIAQgxYMqEXZPRVlUFa2ikTJbZY5qW/TS9nnSrPrRuMWXjCsp6AFnmnryli4g5KQAhBkyZsGuyB1BVmVTZvmnbfpN+jyrHqmOSSy4LC8XJB9UDkAKYK4bkoJxViu5hkz6ApnoAdQXXLESOhSi9Mqdz3EMqEv5dXbcUgGiNNl9oKZZ1mroXTfgApv1/h/6/VlF6df03cXhoF/dACkC0RluDwWahpdgEfQjDaaOA5uE/yLumlZVqU5HWMRdlOdHbfLalAEQjZL00VW3K047unLVRxkUMWcnNo9CPKRrJnGXbL8rvX2cMxzRzZNRBCkBMTd6DXuVhriLw5iXPUBFDVXJDVkxlTOOIrjt5TFbUUVEdun62pQDE1BSFA4YKiyoCb6jCsUmK4sn7ZFbv/bSDzPKWKoK5aBxCrAy6vr+hCmALQuRw+nR2+ZkzcP75MJmAGezdC8eOwfJy+DGyyo8ehW3bNpZt2xaVzwt79mSXm8Hqard1SVLlfxoSR47A2bMby86ejcqT5N33hYXs8rztQ+vgXPS5tgYHD8LVVw/02Q7REn0t6gFUp44dN2+fJkLa6uZ3mUc7tHPFPpQ+W9uz2gOYdpBZ1QFjVeqQvo9dPtvIBDQ+6thxi/YJybi4sFD8QM+ybbktmjA7NM2s/k9NDDKbVjCH5CLq+r+VAhghdVpxISFtocm2ilL3znOrvipDbW3P4v80BMUV0lDq+r+VAhghdSINyuY3LXNi9f2gzyJDEFrzxBAUV1YuoT7/21AFICfwHJHnuNq5s/o+ED2+RU6sLIbuNBwCy8uR03zv3nInuihneRkefRReeCH67OM+3nFH9L6kWVgY9n8rBTBHHD0Ki4uby//mb/IjTPL2SXL2bPSAHzgQCawiqkRPjJkhCC3RHHkNnxdeGPZ/KwUwRywvw3d91+byZ5/dHBZXtk+a06fzWzkxgwhrE6JBDh+GrVujhs/WrdH3LPIaPlnlq6uwbx9s2RJ99hn+KwUwZzz9dHZ5kWkmb58ke/YUH0NmDDGLFAnjw4fhwx+Gc+ei7+fORd+zlEDoGJbV1cikura20cTamxIocxIALwLuBb4IPAT8qi9/CXAPcBL4BLDky8/z30/53/cljvUeX34CeEPZueUErk6TkUBpJ9ZQo1eEqEOZMz4vTcTCQv7xpp0boSmHNk1FAQEGbPfri16oXwncBlzjy28EDvn1w8CNfv0a4BN+/WVeiZznlcfDwELRuaUAqtPUWID0UPa6xxZiqJQJ46JGUV2KIvWafL8aUwAbNoZtwJ8BPwQ8BWz15a8C7vTrdwKv8utb/XbmW//vSRzrO9vlLVIA9WhyNHATxxZiiJSFTVftAYRQpHSa7GGHKoAgH4CZLZjZ/cATwF2+9f5N59zzfpPHgEv8+iXAV7156XngW8AkWZ6xT/JcB83suJkdf/LJJ0OqN3O07QSqE2ESuo+iV0QdQp75Nt+LrGOXOW4PHsz+/fzz69etyFfQSz6mEC0RL8BFwOeBHwFOJcovA77k1x8CLk389jCRAvgQ8NZE+U3ATxWdbx57ALNkRlFrXzRBaP78NmeZq5sHKG+A1zR1q5p7q80eQCUFEB2X9wK/iExAtZgVR+osKSoxbEKe+Tbfi6JjN+G4bYpB+gCAXcBFfv184I+BnwA+yUYn8GG//k42OoFv8+vfz0Yn8FcYoRN4ViY9mRVFJYZPyDPf5nsx7bG7fGe7jgIK8QHsBj5vZg8AXwDucs7dDvwS8G4zO+VNPDf57W8CJr783cB13tT0EFHk0F8Afwi80zl3LuD8c0WVASN9ENtK19ayf1eqB1GVkGe+zfdi2mN3+c527mML0RJ9LfPYAxiyaWWIWQ3F7DNUH0DZsZNjX9K9gKG8s3mgbKDDpS3natt5zYf+0IvhEvJsthl0UPX8k4lzS0sbn/+ssTFdXkMVpABGRhMtqKLU0IoCEvNMSO+3rAdcFG3UtVKQAhgZea33shm7Qo4hs894GEoLtmtC57socvzmHaMP81GoAlAyuDkhzzl77lz02IUknRrDpOwin8ElKiuhyYFjocENRY7fvGM4t/H72bNRavUhZAPtvZVftKgHEE5TM3b1bYcV/TFLPcCmncYh70/Z8UPfwS58a8gENC5CbZh9jTcYcvSTiJiVMSrONa+ssp7PxcXIGRzaYClKqjhtw6wqoQpAJqA5IT3N4MJC9nZ9jTc4ciTq+iY5ezZ/ohrRPUMfo5Kk6bw5WdN0fuxj8NRTxTH5STPUkSORaSd5jGuvHfhUqiFaoq9FPYD6DK3FPUuty7EytGemiDbNVVUy44bcr+Tx8jKM9tUD6F3IFy1SANMxJJv7LNmXx8yQnpki2lJWVY5b55nuSslKAYhN9Plyz1LrUswGbTzPVYR63V5tF++hFMBA6UoIp88Tkvq2bWaldSnGSxWh3lavton3RApggHTVCq4SjZD3sEpYizFSRahXeZ+b9iuUIQUwQEIerqqCN2v7qvHIWU6rvnsLYn6YJg9Q1w2Rqs9+6LWFOoubchJLAQyQsu5lnYcva/sqwj/rHHLYiqoUCfC6mUD7Mls2rXRCG35F727VaDkpgAFS9iBUFbxF+X/yHqK8Byx5DoVsiioUCflpZgPrKmSybULep7Jee1s9AA0E65CyXDtVB7cU5f/JOs+11+bXLXmsWRoQJPqnaJBfyLNb9ByX7TsLhLxPRdfUZj4uKYAOyRpteOzY+gjDqoI3rzw+bvo8N9wQrZcdS0nhRBWKhPw0s4ENbTR7XULep507s/ddWNgoIxonpJvQ1zJvJqAymvIBFNks64xeVBSQKKLIzDOLPoA2KHqfVlaivEPp+7e0VP9akQ9gNmkiCqjqOfqYsELMD2VCfpaigPogT4FOJvWPKQUgNpD3Ih06NHvznYrhMQZB3STJ+9VU5E8SKYCRUdbFzOtiVx0gJoSYjtDU7dO8g6EKQE7gOaBsJqe8KI1jx6Lts0g79pqcfUmIMZP1PqbpKuhia/unEHVZXV0PpduzZ/2BiMviyIEzZzbvm8y1v7aWffy8MDvYGGkRK5j4oY0VDLQYnSDEnFIU8mm2/q538W6Zy2sCDoD9+/e748eP912NxsgS6Hl/clroAiwuRg/Is8+Gn3PbtvzWxsJCthIwg1tvXa/bvn3ZSmTv3miiDCFEOF28T2Z2n3Nuf9l2MgF1RNUJt7O6ic89V034b9mSL/y3bYvOn45PNosGjCUVU9OzLwkxZoY0zkYKoCOqTok4rXBdWIimsssjHhiWHjB2661ReZK8QSqzNiBHiCFQNiC0S0oVgJldZmafN7Mvm9lDZvYuX77TzO4ys5P+c4cvNzP7gJmdMrMHzOyKxLEO+O1PmtmB9i5reFRtRU8rXIvs+wsL8La3RV1RiLqdefOerq7CM89sPsbSkkYGC1GX5eXi964rQnoAzwP/xjn3fcCVwDvN7GXAdcDdzrmXAnf77wBXAS/1y0HgwxApDOC9wA8BrwTeGyuNodNEBEzVdA5Z3cSmOHcuzAwFUQ/luec2l194oRzA84oivkZESKxocgE+DbwOOAHs9mW7gRN+/SPAWxLbn/C/vwX4SKJ8w3ZZyxDGATSVGz90SHx6hG5eRsQml6J4Y2UGHReaC2I+oI1xAGa2D/hB4B7gxc65x70SeRz4br/ZJcBXE7s95svyygdFuvXzrndVs93nUWb3y3IS33ILvPa10fZJ0t/zCN2uyN+gzKDjoqqvSsw2wQrAzLYDvw/8vHMuwyq8vmlGmSsoT5/noJkdN7PjTz75ZGj1GiFLCGfF2EM1J22sVN72tuj7rbdutPutrsKBA9kv3uc+t3Gwlln+4K0kS0tRNE9S4Uwm2dsWCfMhRSyI9lHE18j6BlysAAAOaklEQVQI6SYAi8CdwLsTZXNnAqoylWLoMO2QRFl1ZvEqWiaTbNNSbLqp2r1XnpfxoNng5gOaygVE1HL/OPBfUuW/CVzn168D3u/Xfxz4rN/vSuBeX74TeATY4ZdHgJ1F5+5aARQlZqprE607C1jVxSx8PtL4OiXMRZq85+XQob5rJqrQpAL4YSJTzQPA/X65GpgQRf+c9J873brC+BDwMPAlYH/iWO8ATvnlZ8rOPZQewGRSvwVcNg1jky3/KtekFp3IYywZYue5Z9uYAuhz6VoBtBEB0bSQL1ImWVSJ4pnnF0KEM4ZGw7xHO4UqAI0ETtDGCL2jR8OjcepS5JQNjeKpmqpCzC9jcAQr2ilCCiBFkyP04uRvzjVTtyxFMpkUK6nQKB69ECJmDKG/Y1ByIUgBNEAc5mkGW7dGnxdfDO94R34q5qps27Y5rHNlBZ56qlhJpXs1kwmcf/56Koi4ha8XQsSMIfR3DEouiBA7UV/LEEYCl9FGGCdEk0RPJtF6PBp4Wrt8UURQ3ojjELuvfAfzx7z/p/IByAmcSdUHv46TN2si9qyJ2Zt+SKvWNXSMwDy/SGJ+mWclJwVQgzrCLHTsQLpFHfLwNR2NEVLXhYVqL8QYIkbEeJgXpSAF4FlZWTelwOZRsknqCLOqrerJJGrthyiaphOxhdS16rGVLE7MC/PUmw1VAHPtBD58GN761o35fM6ciZyzWeGNdRyhVdM2nzkDN94YFnHTtKMqpK5Vjy1nmpgXxhgJN7cKYHU1ErRZPPts9p+aJ7TyZsRaXc3OFlqGc9nlaUXTdDRGMiIoizrHHkPEiBgHo4yEC+km9LVMYwIKMXekWVmJom/S2y0tbe4GHjpUzfRTxT+QrlPTNsmm873Mi91UjJs8mbGwMHvPNGP3AZQ5PPOSpyX9BXnCeWWluvM36/x92RpDfB0S6mJsFIV0z5ovYPQKIKQHkNXiDnFqTpvfZ2lpPewzbmFApHyajv3Pouwa58kZJkQVVlamGxMzFEIVwNz6AEIcnmtrm+c+zfMDOLe+zbQ2wQsvhBtuWK9jPIH7mTPrDuu4rI2cPGWO2zE6w4SAyE/2wgvZv82jL2BuFUCZwzMmmfzsHe+Aq6/OVxyxMM5zCofy9NPRZ5agzaJp4VvmuB2lM0wIz5gi2+ZWAcB6YreVlbBQzWefhdtuK1YcscBeXNz82/btcOhQudKJH6QqArVJ4VuW9XRML4AQaUYV2RZiJ+prmcYHkHZiplMtFNnoY4ocvUtLm7+nbeRltvQ2pqBsAvkAxNiZ9SAIxuwELhJgyflxyxRAUVhYqJAuepBCE8n1IXxn/QUQYlZo410btQIomtqxTOBOJuvHyVMkefvWSX+Q/PO7igISQgyDtnrboQpgLn0AefbyM2fKna4//dPr63m28jwbfx0beXICmqeeihbn4Pnno89pJ6URQgyXviPu5lIBTOOsvOWWjSGXWTOEjcpJJIRojb4j7uZSAeQJ6MmkfN+zZ6P8PkW0MXewEGJ89B1xN5cKIE9AX3992P5nzmQPvIqnftyyJeqiHT3azNzBQohxktVYNYvGI3WBRf6CYbJ//353/PjxRo958cUb00PnsXdvJNhjVlejQWBJe922bWr5CyGm4/DhKHNxUhRPK1vM7D7n3P6y7eayB1DE9ddHGraMtA0uz1nz9rfXT9OQ7FEkJ2gXoi30zA2PO+7YKPyhO0fw6HoAEKYA0j2ALVs2/0kxS0tw883VtLV6FKJr9MwNkzzZYpafl6gM9QAKKEvVkBXRU+SUyZtgpoi+w7/E+NAzN0z6dASPUgHkOV4gP6KnLMSzathW3+FfYnzomRsWsTlubW2zVaKrsPJSBWBmN5vZE2b2YKJsp5ndZWYn/ecOX25m9gEzO2VmD5jZFYl9DvjtT5rZgXYuJ6LMzpkVJXTttdHn6dNRi2h1dXPUz/bt+efcubOabbXv8C8xPvTMDYfYHLe2Fn13rrwR2gplQ4WB1wBXAA8myt4PXOfXrwN+w69fDXwWMOBK4B5fvhP4iv/c4dd3lJ27TiqIvKHV6WRwZTl5tmzZnOphcTE7D9DCwubkcGXDuZVwTXSNnrnhEDIr3zTQZC4gYF9KAZwAdvv13cAJv/4R4C3p7YC3AB9JlG/YLm+powDybmzRFIxVsnIm8/Vkfa/yZyrhmugaPXPDIGTmwWkIVQBBUUBmtg+43Tn3A/77N51zFyV+/4ZzboeZ3Q68zzn3J778buCXgNcCL3LO/Xtf/svA/3XO/VbGuQ4CBwH27NnzirW4jxRIUbROmjjSp8o+WZ75Nrz4Qoj5Jbb9p0lHH9alryigrABLV1C+udC5Y865/c65/bt27apcgSr2zNj5VWWfrG1lWxVCVGEo+cTqKoCvm9luAP/5hC9/DLgssd2lwNcKyhunKMInTSygQ+YPjo+T9QcN5c8UQswGQ8knVlcBfAaII3kOAJ9OlL/dRwNdCXzLOfc4cCfwejPb4SOGXu/LGicvwqco30Z6n8kkGtyV3v7aa7P/oKH8mUKI2SEr03DXhISB/i7wv4HvMbPHzOxngfcBrzOzk8Dr/HeAO4gifE4BHwUOAzjnngZ+HfiCX37Nl3XCq18NBw5s7Ak4tzH1czov/803bxTot94KN9yQf44h/JldoFQCQswPc5cKIm+4+/nnZyeBa8rpMgaUSkCI2WC0qSDyhrvnZQDVKMhwlEpAiGbpu0c9dwqgqkBXpE44SiUgxGbqCvHkaGDnos+DB7tVAnOnAPIE+mRSHqnTtzYeOgp3FWIj0wjxIfSo504B5IVkXn99caTOELTx0FG4qxAbmUaID6JHHTJcuK+lTioI5+oNd287N0eT9DmcX6kExJDo+3mcJqVDmzKHJnMB9bXUVQB1aDs3x7TED3pZXiMhxsIQkttNI8TbrH+oApg7E1Bdhmzfzkodm0SROGKMDMGGPo1ZdAgDSOduHEBdhhzjnpc4KokSz4mxMZQkjKurkdI5fTpqMB492r/MGO04gLoUaeO+o4NCnEJD6KkI0SVD6bXPchYAKYAEWX/kEKKDyh5oReKIMaKotOmRAihhqHbGXqaPE2JADMGGPuvIB1BC0WQxXd66IdoZhRDDRD6Ahsgzv5h1awaaZTujEGKYSAGUcPRo9oQyzin0Uggx20gBlLC8nG/qURI0IcQsIwUQwN692eUKvRRCzDJSAAEo3EwIMY9IAQSgcDMhxDyyte8KzArLyxL4Qoj5Qj0AIYQYKVIAQggxUqQAhBBipEgBCCHESJECEEKIkTLoZHBm9iRQMhVKIRcDTzVUnbaYhTqC6tk0qmdzzEIdodt67nXO7SrbaNAKYFrM7HhIRrw+mYU6gurZNKpnc8xCHWGY9ZQJSAghRooUgBBCjJR5VwDH+q5AALNQR1A9m0b1bI5ZqCMMsJ5z7QMQQgiRz7z3AIQQQuQgBSCEECNlLhWAmb3RzE6Y2Skzu67v+iQxs0fN7Etmdr+ZHfdlO83sLjM76T939FCvm83sCTN7MFGWWS+L+IC/vw+Y2RU91/NXzOz/+Ht6v5ldnfjtPb6eJ8zsDR3V8TIz+7yZfdnMHjKzd/nyQd3PgnoO7X6+yMzuNbMv+nr+qi9/iZnd4+/nJ8xsyZef57+f8r/v67GOv2NmjyTu5ct9eW/v0Aacc3O1AAvAw8DlwBLwReBlfdcrUb9HgYtTZe8HrvPr1wG/0UO9XgNcATxYVi/gauCzgAFXAvf0XM9fAX4hY9uX+f//POAl/rlY6KCOu4Er/PqFwF/5ugzqfhbUc2j304Dtfn0RuMffp9uAa3z5jcAhv34YuNGvXwN8osc6/g7w5ozte3uHkss89gBeCZxyzn3FOfcs8HvAm3quUxlvAm7x67cAP9l1BZxz/xN4OlWcV683AR93EX8KXGRmu3usZx5vAn7POfd3zrlHgFNEz0erOOced879mV//G+DLwCUM7H4W1DOPvu6nc859239d9IsDfhT4lC9P38/4Pn8K+DEzs57qmEdv71CSeVQAlwBfTXx/jOKHumsc8D/M7D4zO+jLXuycexyilxL47t5qt5G8eg3xHv+c70rfnDCh9V5Pb374QaIW4WDvZ6qeMLD7aWYLZnY/8ARwF1Hv45vOuecz6vKdevrfvwVMuq6jcy6+l0f9vfxtMzsvXceM+nfGPCqALE0/pFjXVzvnrgCuAt5pZq/pu0I1GNo9/jDwD4CXA48D/8mX91pPM9sO/D7w8865Z4o2zSjrs56Du5/OuXPOuZcDlxL1Or6voC691DNdRzP7AeA9wPcC/wTYCfxSn3VMM48K4DHgssT3S4Gv9VSXTTjnvuY/nwD+O9HD/PW4++c/n+ivhhvIq9eg7rFz7uv+5XsB+CjrZone6mlmi0RCddU59we+eHD3M6ueQ7yfMc65bwJ/RGQ3v8jM4mltk3X5Tj3973+PcLNhk3V8ozezOefc3wEfY0D3EuZTAXwBeKmPEFgicgJ9puc6AWBmF5jZhfE68HrgQaL6HfCbHQA+3U8NN5FXr88Ab/eRDFcC34pNG32Qsp3+c6J7ClE9r/FRIS8BXgrc20F9DLgJ+LJz7j8nfhrU/cyr5wDv5y4zu8ivnw/8UyJ/xeeBN/vN0vczvs9vBj7nvOe14zr+ZULhG5GPInkv+3+H+vA8t70Qedj/ishOeKTv+iTqdTlRFMUXgYfiuhHZJ+8GTvrPnT3U7XeJuvvPEbVOfjavXkTd1w/5+/slYH/P9bzV1+MBohdrd2L7I76eJ4CrOqrjDxN15x8A7vfL1UO7nwX1HNr9/EfAn/v6PAj8O19+OZECOgV8EjjPl7/Ifz/lf7+8xzp+zt/LB4EV1iOFenuHkotSQQghxEiZRxOQEEKIAKQAhBBipEgBCCHESJECEEKIkSIFIIQQI0UKQAghRooUgBBCjJT/D4oPXMBso0ndAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b76d3d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data_train.shape[0]), data_train['cnt'].values, color = 'blue')\n",
    "plt.title('cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分数据有一定分布规律，感觉靠近最高点6000附近和2000以下的部分有一些是噪声点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b76d9f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data_train.temp.values, bins = 30, kde = True)\n",
    "plt.xlabel('Temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Temperature')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b75ccd898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data_train.shape[0]), data_train['temp'].values, color = 'blue')\n",
    "plt.title('Temperature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "气温的散点图和y值有些相像，可能相关性比较强，散点图比较符合季节更替实际情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b75c7eac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data_train.atemp.values, bins = 30, kde = True)\n",
    "plt.xlabel('Apparent Temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Apparent Temperature')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b75bf8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data_train.shape[0]),data_train['atemp'].values, color = 'blue')\n",
    "plt.title('Apparent Temperature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和气温的直方图和散点图分布几乎一致，应有很强相关性，也比较符合常理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b75bca2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data_train.hum.values, bins = 30, kde = True)\n",
    "plt.xlabel('Humidity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Humidity')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b76eadef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data_train.shape[0]), data_train['hum'].values, color = 'blue')\n",
    "plt.title('Humidity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "湿度比较符合正态分布，有部分离群点，直方图左侧有长尾。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b75bc49e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data_train.windspeed.values, bins = 30, kde = True)\n",
    "plt.xlabel('Wind Speed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Wind Speed')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b76e9f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data_train.shape[0]), data_train['windspeed'].values, color = 'blue')\n",
    "plt.title('Wind Speed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "风速也比较符合正态分布，有少数值很大的离群点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = data_train.columns.drop(['dteday','yr'])\n",
    "\n",
    "data_corr = data_train.drop(['dteday','yr'],axis=1).corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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izJ0zlT59BxIZFcNff60CYPny1fzy86Q8X88Dvr7NCQ09QlzcVXJDQmw8xTzS/m44u7uQGHc9U7uKjZ6n9YDOTOs+BsP9lNTtG6YvZ8N00yCr15T3uXIuJtOxuSkxUz0luJFFPQ+ErdxF53GvZ7s/N8THXsPFI+1Dti7uLiTExWdqV6VRdfwHdGF8989IMfdB+doVea5eZVoE+uJYyBE7ezuS7iTxx9eZB5HWFB97zWKZiot7CRIuZ66haqPqdBjQlfEBaTVUqP0cFetVpmWgL46FzTXcTmJJLtaQFB1PwXTXjaN7CZJi064buyKOOD1XiobLRgFQoKQz9ecOZW+fiRSr5YO73wtU+ew17J0KoRk1jPeSOT9rXa7ltyobrgu3prw0c57+ZZ0BsNM0LQWoDyzFtM58TTbHzgEGaJpWDRgDOKbbl/68eXPxcx5U1bMEF+NvEnX9FskpBtYeuUDTSpYfQHF3LsSes6Yn7rNXErmfYqR44QJEXb+V+gHQ6ITbXLh2E49ihTP9DJHZgQOHKVfemzJlvLC3t6dLVz+CV22waBO8aiOv9uwCQKfOL7N16y4AypTxSv0AaKlSHlSoWI4LFyMZM/pbKldsRLUqTejXZyDbtu6yysAcTDP3Pj5l8fYuhb29PQEBHQkKWm/RJihoPYGB3QDo8kp7tmzZ8cjzjvl8GM7OTgwZMjrHM+/bf8gic/eAjgQFWT5xBQWtS8vcpT2bzZmDgtbRPaAjDg4OeHuXwsenLHv3hTJy5ATKlqtLhYov0rPXu2zevIM+fQcC8Pffa2jerBEATZo04NSps3m+nge6d++Ua0taAC6FnaGktxsuXiXR2+up5d+QY+sPWLTxrOpNt/Fv8ssb33Lr2o3U7UqnKFSsCADulUrjUak0J7YfzrXsWYkMO0MJbzeKm+up4d+AiAz1lPBOW0ZUqUUtrp6PzXiaXHUu7DSu3u484/Usens7XvBvTOj6/RZtSlctS7/xbzP5jQncTNcHPw2awuBG7zC0cX8WjZ/HjmVbc31gDnA27DRuZd0pWcpUw4v+jTm4fp9FmzJVy9Lvq3f4/vWvuHEt7csPfvxgMh82fJvBjd/h9y/nErJsS64OzAESDp2hcDk3CpYuibLX49GpAbHr0q6blJt3WVv1LTbWG8jGegO5fvA0e/tMJDHsLDs7jUndfvbn1ZyauvzpGZg/xfLSzHkmSqkiQCFN04KVUruB0+ZdN4H0n+QrCsQopeyBnsCjPqmU8fhcN2z0BPaFHiYh4QYtO/Xi3dcD6ZLhQ1u2ZKfX8XH7uvSftxmjUaNj7XL4PFuMGRsPU8XThWaVvBjsW5uxK/awcOdxUDCm84sopQi9cIVZ28Ox0yt0SjHCry7FCzs++ofmsrzYBwaDgWFDPuevFXPR63XMn/cHxyNO8enIQRw8eITVwRuZN3cxM3+ZxKHDm7h+PZF+fUyDvgYN6/Lh4HdITknBaDQyeNAo4q9lPytnrfyDBn3GqqCF6PQ65s5ZTHjESUaPGsqBg2EEBa1n9uxFzJk9hfDwEK7HJ9Ar8N3U40+e2IWTU1EcHOzp4N+W9u1f48bNW4wY8QHHj59i7x7T6/MZP85h9uycWXJmMBj4YNBIVq36Db1Ox5y5iwkPP8no0UM5cMCUedbsRcyZM5WI8BCuX0+gZy9T5vDwk/zx50oOh20mxWBg4AefPnJd/zffTGfe3Gl88MGb3Lp1h7ffGZYjdVi7noIFHWnVsgnvvvtRjuZ9GKPByNJRs3l73ifo9Dr2LNlM7KlIfD/sxqUjZzm24QAdRvSkQKEC9J0xCIDrUVf59c2J6O3teP+PzwFIunWXBR9Os/myFqPByIpRc3h93gh0eh37lmzh8qlIWn/Ylcgj54jYcICGfdpQoVE1DCkp3E28zZIhP9o88/xRvzBs3mfo9Dq2LdlE1KlLdP6wB+ePnCZ0w356jOhNgUKOvDdjCADxUVeZ/OaER5w59xgNRuaN+oVh80aZa9hI1KlLvDK4B+cOnyF0wz56fNIbx0KOvD9jKADXoq/y/Rtf2Ti5iWYwcvSTObz4+wiUXsel37dw60Qkzw3vSsKhc1xed+DRJ3laPaXfc64eZ62n1X64Urc0TSuilGoGDNU0zc+8fRqwH9PylBWYZsIVMFHTtLlKqUbAz5hmxbsCbYDhwAXgCFBU07S+Sqk5QJCmaX9m+Hn2mGbhnwHmZLXu/IGnYVlLysb5to7wr9m1tO3XiOWEEmVa2TrCE0lKuW/rCE/Mln/nBAzweMnWEZ6YQz5/4zWW/P17bHwKfocD7hWwdYQn5h/7e576Rbh3MsSqF0aBio1tUq9NZ841TSti/u8WYEu67QPSNbP8viPT/h1YfpXij+ZbxnZ9s/l5yUDLfx1cCCGEEELYlvHp/LKJvLTmXAghhBBCiP+0PL3mXAghhBBCiCw9pWvOZeZcCCGEEEKIPEJmzoUQQgghRP4j33MuhBBCCCGEsCaZORdCCCGEEPmPrDkXQgghhBBCWJPMnAshhBBCiPznKV1zLoNzIYQQQgiR72ia/E+IhBBCCCGEEFYkM+dCCCGEECL/kQ+ECiGEEEIIIaxJZs6FEEIIIUT+85R+IFRmzoUQQgghhMgjZOb8P8CuZaCtIzyRlE0LbB3hidxJvmfrCE9Ep5StI/znFXJwtHWEJ1LBkP+fauw1Wyd4MnH5vQuegr9DFYsl2DrC0+cpXXOe339drS5l43xbR3gi+X1gLoQQQgjxXyKDcyGEEEIIkf8Y5XvOhRBCCCGEEFYkM+dCCCGEECL/eUrXnMvMuRBCCCGEEHmEzJwLIYQQQoj8R77nXAghhBBCCGFNMnMuhBBCCCHyH1lzLoQQQgghhLAmmTkXQgghhBD5j6w5F0IIIYQQQliTzJwLIYQQQoj8R2bOhRBCCCGEENYkM+dCCCGEECLf0TSDrSNYhQzOhRBCCCFE/iPLWsQ/teNUNB2nrMR/8t/M2nYs0/6YhNu8MWsD3Wesptv0YLafjALgSORVAmYEm27Tg9kUfim3oz+WkeMn0aR9Dzr1esfWUbK141Q0HSf/jf/3Kx7eB9OD6TZtlWUfTDc9/gHTVlmlD9q2acaxo9s4Hh7C8GHvZdrv4ODAbwt/5Hh4CDtDVlKmjFfqvo+GD+B4eAjHjm6jTeumjzznvLk/cOzoNg6FbuTnmd9hZ2d6Xf7cc+UJ2fY3t2+eZfCHb+dIXW3aNOPoka2Eh4cwbGjWdS1cMIPw8BBCtqfV5eJSjHVrlxB/7QSTJ49LbV+woCPLl8/lyOEtHArdyJfjRuRIzkyZj24jIjyEYdn0xcKFPxIRHsKODH0xfPgAIsJDOHp0G63NfVGxYnn271uXert29TgD338DgC5d/Dh0aBP3ki5Rp3b1HK8FoGWrJuw/uJ7QsE18ODhzvzo4ODB77lRCwzaxcfNSSpf2BKB2neps37mS7TtXErIrCD//NqnHODsXZd6Caew7uI69B9ZSr34tq2TPqFSz6ry65Vt6bv+OWu/6Z9pftVcLuq//ioA1X9J56WcUr+ABQIVODQlY82Xqrf+FeZSoUjpXMmfk1aw63bZ+S0DId9R4L3MNlXu1oMuGr3hl7Zf4L/uMYuYalJ2ept+/TZcNX9F189dZHpsbnm9ak/EbpzJhyzTa9e+caX+b1/0Zt34yY1dPYtjC0ZTwLJm679czSxgTPJExwRMZ+PPHuRnbQrWmNZmwcSrfbJlG+yxqaPu6P+PXT2bc6kkMz1ADgGORgkzePZPAMW/kVmQLhRrXoezqnym79ldc3uyWab9T51aU37mIMn9No8xf03Du2tZiv65wIcptnc+zn/XPrcjiCcjMuZUYjEa+CtrP//VpgatTQXr+tJamlbwo/6xzapuftx6lzfNlCKhfgTNxiQxYsIXVgz3xebYYv73ti51ex5WbdwmYEUyT5zyx0+et11Kd2rXmtS4d+OSLibaOkiWD0chXK/fxf31b4OpUiJ7/tyabPihNQP2Kpj6Yv5nVQ8x98E66Ppi+Kkf7QKfTMXXKl/i2e5XIyBh27wpmZdA6IiJOpbb5X79XuX49kUpVGhMQ0IGvxn/Kaz37U7lyBQICOlK9Zgs8PFxZu3oRlau+BJDtOX///S9693kfgAXzp/P6/17jp5nziI9PYNCHn9Gxo2+O1TVlyjjatXuNyMgYdu1cRVDQOiKOp9XVr18PrickUqVKYwK6dWD8l5/Qs9e7JCXd4/Mx31K16nNUrVrJ4rzff/8TW7fuxN7enrVrFtG2bXPWrt2cY5mnTvmSl9M9bkFZ9EXC9UQqm/ti/PhP6Wnui+4BHalh7os1qxdRpepLnDx5hrr12qSe/8L5AyxfsRqAY8eOExDwJjOmT8iR/FnV892kz+nUoQ9RUbFs3vYXwcEbOXH8dGqb3n26kZCQSK0aLejS1Y8xX3xEvz4DiQg/SbOXOmEwGHB1LcmO3atYHbwRg8HAhG9GsWH9Nnr3GoC9vT2FCjlaJX96SqdoMq4PK1+bwK2YeLoGjeX8+gNcPxWd2ubk8l0cW7AJAO/WtWk0qhdBgd9wavlOTi3fCYBLJS9e/mUw18IvWj1zVjU0GteH4NcmcDsmnk6rxnJh3QES0tVwevkuIsw1lG5dmxdH92JNr28o51cfvYMdS1uNQO/oQLfNX3NmxS5uRV7Nxfw6Ase+ycReY4mPvcaov7/m0Pp9RJ+OTG1zMfwcY/2Hcz/pPs17tSVgRCA/DpgEwP2k+4xuNzTX8mZF6XT0Hvsm35hr+PzvrwnNUMOF8HN8bq6hRa+2dB8RyAxzDQBdhrzK8T3htogPOh2uo94j8n+fkHz5KmX+mMKtTXu4f8byer65eitxX/yY5Sme+SCQu/uO5Eba3CX/EyLxTxyNvEYplyJ4uRTB3k5P22pl2HI80qKNUorb95IBuJV0n5JFCwJQ0MEudRB4P8WAQuVu+MdUt2Y1nJ2K2jpGto5GXqNUiaJ4uRRN64MIyxlwBdxOyv0+qF+vFmfOnOfcuYskJyezZMkKOvhbznR08G/D/Pl/ALB06SpaNG9s3t6WJUtWcP/+fc6fv8SZM+epX6/WQ8+5es2m1PPu23cILy93AK5cucb+A2EkJyfnSF316tXMlME/3ewrgH/6upatorm5rjt37rJz5z6Sku5ZtL97N4mtW02DrOTkZEIPHcXT0z1H8kLmvli8ZAX+GfrCP5u+8Pdvy+Is+iK9Fi0ac/bsBS5eNL0rc/z4aU6ePJNj+TOqU7cGZ89e4Pz5SyQnJ7PszyDat29l0aZd+1b8tnAZAMv/Wk3TZg0A02NtMJjWcDo6FkDTNACKFi1Co0b1mDd3CWDqh8TEm1ar4YFna5Yn8fxlbly8gjHZwOm/d1O2TR2LNsm37qb+265QWub0KnRsyOm/d1k9b1ZK1izPjfOXuWmu4cyK3ZR5SA32hQrAgxo0U01Kr8PO0QFjcopF29xQrqYPcRdiuXLpMobkFPauDKFWm3oWbY7vOsr9pPsAnAk9SXG3Erma8VHK1fThcroa9qwMofZDajgdehKXdDV4P18Op2ecObo9LFdzP+BYvSLJF6NJjoyF5BRuBm+lSMsXH/v4AlV90Jcozu0dB62YUuQkmw7OlVKFlVKrlFJhSqmjSqnuSqk6SqmtSqkDSqm1Sil3c9s3lVL7zG2XKqUKmbd3Mx8bppTaZt7mqJSarZQ6opQKVUo1N2/vq5RappRao5Q6pZT6xlq1xd28i5tz4dT7rk6FiLtxx6LNO82rsSrsHG0m/sWABVv4uH3d1H1HLl3llR9W0XV6MCP96+W5WfP8IO7GXdycC6Xed3UuRNxNyye2d1pUN/XBt8sYMD+LPpgaRNdpqxjZoX6O9oGHpxuXItNmziKjYvDwcMu2jcFgIDHxBiVKFMfDI4tjPd0e65x2dnb07Nklx2adM/L0cCfyUkzq/aioWDwyDKQ9PdyIjDS1MRgMJN4w1fU4nJ2daN++FZs3h+RYZg9PNyLTPW5RUTF4PmZfmGqxPNbD0/LY7gEdWbx4eY7lfRQPD1eiIi37wN3D1aKNu4dbahuDwcCNxJu4mPugTt0a7N63mp17gvnwg88wGAx4e5fi6tV4ZvzfN2zf8Tc/TBtPoUIFrV7L9yZrAAAgAElEQVRLYbfi3IqOT71/Kyaewm6Zr5Xn+7SiZ8h3NPykByGj5mXa7+P/AqdW2GZwXti9OLdi0mq4HRtPYffMNVTp04ruId9R/9Me7DTXcHbVXlLu3KPnwWm8uncyh38K5l7C7VzLDlDc1YX46LSZ+viYeIq7Zj/4bhLQkiNb0gaB9gUcGPX314z86ytqtalv1azZ+ac1NA1oyWFzDUopeozsw+Lxma+r3GLn+gzJMVdS76fEXsUui/xFWzfGe8UMPKZ8ip3bM6aNSvHsR29y5dtfcitu7jIarXuzEVuP+HyBaE3Tamia9jywBvgB6KppWh1gFvClue0yTdPqaZpWA4gAXjdvHwW0NW/vYN72HoCmadWAV4G5SqkH78HWBLoD1YDuSqlS1igsi8kblLKcfV1z+DwdapVj3dDOTOvVjJFLd2I0mg6sVuoZlr3fnoVvt+XX7ce4l/x0fiLZmjQyd0LG+e81h8/ToXZ51g17hWmBWfTBQD8Wvu3Lr9tytg8yXgtAphm/rNtkf+zjnHPaD+PZvn0PITv2/tPIjyWLCI9ZVxa/MBno9Xrmz5/O9OmzOHcu55YnWKMvHrC3t8fPrw1/Lg3KgaSPJ7uslm0yH/cg94H9YbxY72WaN+3M4CHvUKCAA3Z2dtSoWZVff1nIS406cPvOXT4cYv3PmjxOLQBH525gYeMh7PpqEXUGdrLY92zN8qTcvU/8icjMB+aKrB7szJvC525gceMh7B2/iFrmGp6tWQ7NaGRhnfdZ1GAw1d5qR9HSJTMfbE3/4Pe1QacmeFcvz+qZK1K3DW34NmM7fMRPAyfz2qh+lCztmuWx1vRP/uY0NNcQbK6hZaAvhzcfJD7mmlUz/mMZ4t/avIezLftyvuO73N4ZituEIQAUe82P21v3kRKbe0uhxJOz9eD8CNBKKfW1UuoloBTwPLBeKXUIGAk8+OTV80qp7UqpI0BPoKp5+w5gjlLqTUBv3tYYmA+gadpx4AJQ0bxvo6ZpiZqmJQHhQJmMoZRSbyml9iul9v+6Yf+/KszVqSCxiWkzHJdv3EldMvHAXwfP0uZ50weUapQuyb0UAwl3LN/SL1fSmYL2dpyOS/hXOf7LXJ0KEZuY9m7F5cQs+uDAmQx9YMzcB886U9AhZ/sgKjKGUl4eqfe9PN2JibmcbRu9Xo+zsxPx8deJisri2OjLjzznZyM/pGTJEgwd9nmO1ZFRZFQMXqXSZso9Pd2IiY7N3Ma8rEav1+Ps5ER8/KMf2x9nfM3p0+f44YdfczRzVGQMXukeN09Pd6Ifsy9MtVgeGxOddqyvb3NCQ48QF5d7T4xRUbF4eln2QWyGeqLTtdHr9Tg5F+V6hj44eeIMt+/cpUqV54iKiiEqKpYD+01v669YvpoaNapibbdi4ini4ZJ6v4i7C3cuX8+2/akVuynb1nLJSIWOL9ps1hzgdkw8RdzTaijs5sLt2OxrOLNiN97mGsp3asilLYfRUgwkXbvB5X0nKVm9nNUzp3c99houHs+k3ndxdyEhLj5TuyqNquM3oAtT3viKlPspqdsT4ky1Xrl0meO7j1Gmalnrh84g/h/U4D+gC5PT1VC+dkVa9X6ZiSE/0uOT3jR6pSndPuqVa9kBUi5fxd497UWZndszpMRZvlgwJtxEMy9PTPxjDY5VKwBQsGZlivX0p9zGOZQc/gZOHVvxzOB+uRfe2jSjdW82YtPBuaZpJ4E6mAbpXwFdgGOaptU036ppmvZgweocYIB5NnwM4Gg+xzuYBvGlgENKqRJkOVWRKv3Iy0AWH4rVNG2mpml1NU2r+3qruhl3P5aqniW4GH+TqOu3SE4xsPbIBZpW8rRo4+5ciD1nTU+aZ68kcj/FSPHCBYi6fosUg+miiE64zYVrN/EoVjjTzxAPV9WzBBevZewDL4s27sUKseeMafB4Ni6R+ymGLPrgFheu3sjRPti3/xA+PmXx9i6Fvb09AQEdWRm0zqLNyqB1BAaaPpXfpUt7Nm/Zkbo9IKAjDg4OeHuXwsenLHv3hT70nP/r9yptWjejZ6/3HmuW+t/avz8sU4agoPUWbYKC1qfV9Up7tpjrepgxnw/D2dmJIUNG53jmjI9b94COBGXoi6Bs+iIoaB3ds+iLB7p375SrS1oADh44TPny3pQp44W9vT2vdPUjOHijRZvg4I281vMVADp1fpltW02D1zJlvNDrTXMcpUp5UKFCWS5cjCQu7ipRUTH4VDANrJo2a2jxAVNriQs7i7O3G0VLlURnr8enw4ucW2+5btbZO20mtkzLmiSeT/diUCnKt3/BZuvNAa6EncWpbFoN5Tu+yMUMNTiVTauhdMuaJJ4z1XA7+hoeDU0vguwKFuDZ2j4knIkmN50LO82z3u484/Usens76vs3JnS95aRV6apl6TP+baa+MYGb126kbi/kVBg7B9NTbJHiRalQpxLRp3L/HYxzYadxTVfDC9nU0G/820zOUMNPg6YwuNE7DG3cn0Xj57Fj2Vb++HpBruZPOnIS+zIe2Hu6gr0dRds15dam3RZt9CXTlkoVafEi98+YPl8VM+wbzrbow9mWfbnyzS/cWLGBq5Nm52Z88S/Y9NtalFIeQLymaQuUUreAt4CSSqkGmqbtUkrZAxU1TTsGFAVizNt6AlHmc5TXNG0PsEcp5Y9pkL7N3GaTUqoiUBo4AdTOrdrs9Do+bl+X/vM2YzRqdKxdDp9nizFj42GqeLrQrJIXg31rM3bFHhbuPA4KxnR+EaUUoReuMGt7OHZ6hU4pRvjVpXhh638zwj81bPQE9oUeJiHhBi079eLd1wPpkuGDdLZkp9fxsV9d+s/dZO6D8vi4FmPGxjCqeJSgWWUvBvvWYeyK3eY+UIx5pYG5D+KYtS0cO70OnYIRfvVytA8MBgMfDBpJ8Krf0Ot0zJm7mPDwk3w+eij7D4QRFLSeWbMXMXfOVI6Hh3D9egKv9XoXgPDwk/z550qOhG0mxWBg4AefYjSvjcvqnAAzpk/gwoVIQrb/DcDy5cGM+3Iyrq4l2bNrNU5ORTAajQx8/02q1WjGzZu3/nVdgwZ9xqqghej0OubOWUx4xElGjxrKgYOmumbPXsSc2VMIDw/henwCvQLfTT3+5IldODkVxcHBng7+bWnf/jVu3LzFiBEfcPz4KfbuWWOq58c5zJ79+79+/DNm/mDQSFZleNxGjx7KgXR9MWfOVCLMfdEzXV/88edKDmfRFwULOtKqZRPeffcji5/XsaMvk78fR8mSLqxYMY+wsGO09+uZI7U8qGfokDEsWz4HvV7Hgvl/cjziFJ+MHETowSOsDt7I/LlLmPnLd4SGbeL69QT+1/cDAF5sUJcPh7xNcnIKmtHIkA9HE3/NNPM5fMgYfvn1e+wd7Dl/7hLv9R+eY5mzoxmMbP9sLv4LhqP0Oo4v3sr1k1HUG9KFK4fPcX79Qar1bYNX46oYUwzcS7zNxg9/Sj3e44VK3IqJ58bFKw/5KdavYednc3l54XCUTscJcw11hnbhStg5Lq4/SNW+bfBMV8NWcw3H5qyn6aS36LpxAijFySXbiI/I3a/WNRqMLBz1C0PmfYZOr2P7kk1En7pEpw97cP7IaQ5t2E/AiN4UKOTIuzNMSymuRV1l6psT8PDxos/4tzFqGjqlWPXjXxbfkJKbNcwf9QvDzDVsW7KJqFOX6GyuIXTDfnqYa3jPXEN81FUmv2mdb1T6xwxG4r74Ea9fx4FOT+LSddw/fZES7weSdPQktzfvoXhgR4o0fxHNYMCYeJPYEd/ZOnXueEq/51xZcxbtkT9cqbbAt4ARSAb6AynAVMAZ04uHyZqm/ayU6g8Mx7RE5QhQVNO0vkqpZUAFTLPlG4FBQAHg/zDNyqcAgzVN26yU6gvU1TRtgPnnBwETNU3bkl3Gu4vH2O4BygF2LQNtHeGJpWzK3VmKnFa010+PbpSH6bJaoJzP2PLvXE4o5JD3Xpz/E18Vb2DrCE/MPn9fQuy0y91veclpT8MQ7BPHO49ulMc9d3x1nnpCuLtuhlV/Mwu2edcm9dp05lzTtLXA2ix2Ncmi7Y9Api/w1DTtlSyOTwL6ZtF2DqblMQ/u+z12WCGEEEIIkXfI95wLIYQQQgghrEn+D6FCCCGEECL/eUrXnMvMuRBCCCGEEHmEzJwLIYQQQoj8R2bOhRBCCCGEENYkM+dCCCGEECL/kW9rEUIIIYQQQliTzJwLIYQQQoj8R9acCyGEEEIIIaxJZs6FEEIIIUT+I2vOhRBCCCGEENYkM+dCCCGEECL/eUrXnMvgXAghhBBC5D+yrEUIIYQQQghhTTJzLoQQQggh8h9Z1vLfZNcy0NYR/vPsWvSydYQncje6FyXKtLJ1jH/tniHZ1hGemKZpto7wRO4m37N1hCdySp9i6whPzAFl6whP5OkcwuQvp68Xs3WEJ/acrQP8R8jg/CmXsmmBrSM8kfw+MBdCCCGElTylM+ey5lwIIYQQQog8QmbOhRBCCCFE/pPPlyxmR2bOhRBCCCGEyCNk5lwIIYQQQuQ/suZcCCGEEEIIYU0ycy6EEEIIIfIfmTkXQgghhBBCWJPMnAshhBBCiPxHk5lzIYQQQgghhBXJzLkQQgghhMh/ZM25EEIIIYQQwppkcC6EEEIIIfIfTbPu7TEopXyVUieUUqeVUh9nsb+0UmqzUipUKXVYKdXuUeeUwbkQQgghhBD/kFJKD0wHXgaqAK8qpapkaDYSWKJpWi2gBzDjUeeVNedCCCGEECL/sf2a8/rAaU3TzgIopRYBHYHwdG00wMn8b2cg+lEnlZlzGxk5fhJN2vegU693bB0lWztORdNx8t/4f7+CWduOZdofk3CbN2ZtoPv0YLpNW8X2k1EAHIm8SsD0YNNt2io2hV/K7eiPJa/2QavWTTgQuoFDhzfx4ZDM2RwcHJg9dyqHDm9i05ZllC7tCUCdOtUJ2RVEyK4gduxehZ9/GwAKFHBg89a/2LF7FXv2reGTTwdZNX+b1s04cngL4ce2M3Tou1nmXzB/BuHHtrN929+UKeMFgItLMdauXcy1q8eZ/P0XFses/Hs++/auJfTgBqb9MB6dLuf/dLVp04yjR7cRER7CsGHvZZl74cIfiQgPYUfIytTcAMOHDyAiPISjR7fRunVTi+N0Oh379q5l+V9zU7fN/GkiB/av5+CB9SxaNJPChQvlTP4jWwkPD2HY0GzyL5hBeHgIIdsz5B/2HuHhIRw9stUi/4ABrxN6cAOHQjfy/vuvp25fuGAG+/auZd/etZw8sYt9e9c+cf7sVGpagxEbJ/HJlsm07N8h0/6mr7fjo/UTGbb6a/ovHElxz2dS9/l9/BrD137L8LXfUtOvgdUy/hMVm9Zg6MbvGLble5plUc8LPVsxaM3XfBD8Fe/8MZpnfTxtkNJStaY1mbBxKt9smUb7/p0z7W/7uj/j109m3OpJDF84mhKeJS32OxYpyOTdMwkc80ZuRc4kv9dQsnkNmu74jma7v6f8+5mvmwfc/OrT/vLvONcoZ7Hd0bMEbc/Oplz/9taO+lRRSr2llNqf7vZWhiaeQPpBTqR5W3qfA72UUpFAMPD+o37uUz04V0oVU0q9m+5+M6VUkC0zPdCpXWv+b9I4W8fIlsFo5KuV+5jeuznL3vdjzeHznIlLtGjz89ajtHm+NIvfa8eEgMaMX7kPAJ9ni/HbO74sea8d0/u04Iu/95BisPmr20zyYh/odDq+mzSGLp37Ua9OW7p28+e5Sj4WbXr3CSAh4QY1q7dg+rRZjPniIwDCw0/StHFHGjfw45VOfZnywzj0ej337t3Hr11PGr3YnkYN/GjVugn16tW0Wv4pU8bRoWNvatRsQfeAjlSqVMGiTb++PUhISKBK1ZeY+sMvfDnuEwCSku4xZsxEPv44c5+81rM/9eq3pVbtVjzzTAm6dPHL8dxTp3yJv38vqtdoTo/unahc2TL3//q9SsL1RCpXacyUqT8zfvynAFSuXIHuAR2pUbMFfn49+WGq5YuHge+/QcTxUxbnGjL0c+rUbU3tOq25dDGKd9/t98T5p0wZh3+HQGrUaE737h2pnPFx79eD6wmJVKnSmKlTf2b8l6bHvXKlCgQEdKRmzRb4+fdi6tQv0el0VK3yHK//71UaNvKjTt02tGvXCh+fsgD07PUu9eq3pV79tvy1PJjly1c/Uf7sKJ2iy9j/MbPvBL5uPYRaHRrhmmGwGhV+nkn+n/Dtyx8RtnoP/iN6AlCleS28qnozsd1HTO40khZv+VGgSEGr5HxcSqfoNLYfs/p+zaTWQ6nRoWGmwfehFTuY7PsRU9qNYOtPQfh9FmijtCZKp6P32Df5ru+XjGg9iBc7NMbDx8uizYXwc3zuP5yRLw9m/+rddB9hmbnLkFc5viccW8n3NegUVSf0Y+9rX7P1paF4dG5IkYqZX7TpCzvi/YYv1w+cyrSvythArmw8lBtpc5fRaNWbpmkzNU2rm+42M0MClUWqjIvVXwXmaJrmBbQD5iulHjr+fqoH50AxIPPUXR5Qt2Y1nJ2K2jpGto5GXqNUiaJ4uRTF3k5P22pl2BJhOQOugNtJyQDcSrpPyaKmJ76CDnbY6U2X1v0UAyrLa9f28mIf1K1bg7NnL3D+/CWSk5NZ+mcQ7f1aW7Rp79eK3xcuBWD5X6tp1qwhAHfvJmEwGABwLFDA4rMst2/fAcDe3g47ezu0x/ygyz9Vr15Nzpw5z7lzF0lOTmbJH3/jb57Bf8Dfvw3zF/wJwLJlq2jevBEAd+7cZefOfSTdu5fpvDdv3gLAzs4OBwf7HM9fv14ti9yLl6zA379t5tzz/wBg6dJVtGje2Ly9LYuXrOD+/fucP3+JM2fOU79eLQA8Pd15+eWWzJr1e5b1ABQs6PjE9WR63JesyPpxf5B/2Sqap+Zvw5IM+evVq0mlSj7s2ROael1t37abjh19M/3srl38WbxkxRPlz07pmj5cvRDLtUtxGJINhK7cyfNt6lq0Ob0rnOSk+wBcCD1FMTcXAFwreHJmTwRGg5H7d+8RFXGRyk1rWCXn4ypV04drF2KJN9cTtnIXVTLUc+/W3dR/OxQq8NgfSrOWcjV9uHwhliuXLmNITmHPyhBqt6ln0eb4rqPcN/fB6dCTuLiVSN3n/Xw5nJ5x5uj2sFzNnV5+r6FYbR/unIvl7oU4tGQD0ct34epbN1O75z4O4Oz0lRjNz8sPuL5clzsX4rh5IjK3IucezWjd26NFAqXS3fci87KV14ElAJqm7QIcgWd4iDw/OFdKeSuljiulflFKHVVKLVRKtVJK7VBKnVJK1VdKfa6UmqWU2qKUOquUGmg+fAJQXil1SCn1rXlbEaXUn+ZzLlRK5c2Ro43F3biLm3PaW+2uzoWIu3nXos07LaqzKuwcbb5dxoD5W/i4fdofiyOXrvLK1CC6TlvFyA71Uwfr4uHcPdyIjIxJvR8dFYOHu2uGNq6pbQwGAzdu3MSlRHHANLjfs28Nu/auZtDAkamDdZ1OR8iuIM6c38fmTTvYv986TzIeHm5cikz7uxQVFYOnh1umNpHmNg/ylzDnf5iglQuIvBTKzVu3WbZsVc7m9kzLlG1uz7TaDAYDiYk3KFGiOJ4emY/18DQd+913YxgxYhzGLNZF/vLzJCIvHeK553yYPn3WE+X39HAn8lLadRMVFYuHp3uGNm4W103iDVN+D093i2suKjIWTw93joWf4KWXXsDFpRgFCzri69sCLy8Pi3M2bvwCcXFXOH363BPlz04xVxcSoq+l3k+MicfZ1SXb9i8ENCdii2l2MDriIpWb1cTe0YHCxYtSoUEVirmXyPbY3ODsWjxDPddwds187TcIbM3wrZNp9/FrrPh8bqb9uam4qwvx0VdT78fHxFPcNfvHsWlASw5vOQiAUooeI/uwePw8q+d8mPxeg6Nbce6mu26Soq/h6GZ53Tg9742jhwtx60MttusLFaD8AH9OTVyaK1n/g/YBFZRSZZVSDpg+8Pl3hjYXgZYASqnKmAbnVx520vwyYvIBpgDVgUrAa0BjYCjwiblNJaAtpsX5o5VS9sDHwBlN02pqmjbM3K4WMAjTp2rLAY0y/rD0a4x+mfd7xt3/CVqmd2Uyv3ez5vB5OtQuz7phrzAtsBkjl+7EaDQdV63UMywb6MfCt335ddsx7iUbciF1/pfVS8WMs6pZvhNhbrN/fxgv1POlWZNODBnanwIFHAAwGo00buBH5YoNqVOnOpWrVMzx7GB6IsscLUP+x6gxK37+vSjjXZcCDg6ps+055fFyZ9Um+2PbtWvFlbirHAw9kuXPfOPNwZQuU5vjx08R0C37NaSP47Gum2xyZnfs8eOn+XbiDFYH/07QygUcPhJOSkqKRbvu3TtabdYcyOYN46yvlTqdGlOqejk2zVwJwInthwnfHMoHy8YSOPV9zh88hdHWy+uyuYYy2jV/Pd80HcTqCb/R8v3M66Nz0+P8bjzQsFMTvKuXJ3im6ZpoGejL4c0HiY+5lmX73JLva3jUHKJSVBkbSMTnCzLtqjisK+d+Wo3hTuZ3JJ8GmlGz6u2RP1/TUoABwFogAtO3shxTSo1VSj34wz4EeFMpFQb8DvTVHvGkl1++reWcpmlHAJRSx4CNmqZpSqkjgDdwCFilado94J5SKg5wzeZcezVNizSf65D5+JD0DcxrimYCJF89a9v3FG3E1akQsYl3Uu9fTryTumzlgb8OnGFGn+YA1ChdknspRhLu3MOliGNqm3LPOlPQwY7TcQlU9bTtrFV+EB0Vi5dX2oynh6c7MbFxlm2iTW2io2PR6/U4ORUlPj7Bos3JE2e4ffsOVao8R2i6wWFi4k1Ctu+hVesmRISfzPH8UVExlEo3u+rp6U50zOUMbWLx8vIgKir7/Nm5d+8eQavW4+/Xho0bt+dc7sgYi1nhLHNHmmqLiopBr9fj7OxEfPx1IqMyHxsTfRk//9b4+bXB17cFjo4FcHIqytw5U+nTd2BqW6PRyJI//mbI4P7MnbfkX+ePjIrBq1TadePp6UZMdGzmNl7uafmdnIiPTzDXnu5YLzeiY0zHzpmziDlzFgHwxdiPiIxKm2HX6/V06vgyLzZ45Ff2/msJsfEU80j7u+Hs7kJi3PVM7So2ep7WAzozrfsYDPfTXkBsmL6cDdOXA9BryvtcOReT6djclJipnhLcyKKeB8JW7qLzuNez3Z8b4mOv4eKR9g68i7sLCXHxmdpVaVQd/wFdGN/9M1LMfVC+dkWeq1eZFoG+OBZyxM7ejqQ7SfzxdeZBpDXl9xqSYuIpmO66cfQoQVJs2nVjV8SRopVK8eKyUQAUeNaZuvOGsr/3RIrV9sHN7wUqffYa9s6F0IwahnvJXJi1LtfyP+00TQvG9EHP9NtGpft3OFlMBD9Mfpk5T/+Sz5juvpG0Fxjp2xjI/oXH47b7T6vqWYKL124Sdf0WySkG1h65QNNKlh+gcS9WiD1nTE/iZ+MSuZ9ioHjhAkRdv5X6AdDohFtcuHoDj2KFc72G/OjAgcOUK+9NmTJe2Nvb06WrH8GrNli0CV61kVd7dgGgU+eX2bp1FwBlynih1+sBKFXKgwoVy3HhYiQlnnHB2dm0tt7RsQDNmjfi1ImzVsm/f38YPj7eeHuXwt7enoBuHQgKWm/RJihoPYG9ugLwyivt2bJlx0PPWbhwIdzcngVMA0Lfti04ceJ0jubet/8QPj5lU3N3D+hIUJDlk1dQ0DoCA7sB0KVLezabcwcFraN7QEccHBzw9i6Fj09Z9u4LZeTICZQtV5cKFV+kZ6932bx5R+rAvHx579Tz+rVv/cT1mB73tPwBAR2zftwf5E/3uAcFrScgQ/59+0xLQ0qWNA0ISpXyoFOnl1m8OG2WvGXLlzhx4gxRUdYb8F4KO0NJbzdcvEqit9dTy78hx9YfsGjjWdWbbuPf5Jc3vuXWtRup25VOUahYEQDcK5XGo1JpTmw/bLWsjyMy7AwlvN0obq6nhn8DIjLUU8I7bTlVpRa1uHo+NuNpctW5sNO4ervzjNez6O3teMG/MaHr91u0KV21LP3Gv83kNyZwM10f/DRoCoMbvcPQxv1ZNH4eO5ZtzfWBOeT/GhJDz1C4nBsFS5dE2evx6NSAy2vTrpuUm3dZX+UtNtcbyOZ6A0k4cJr9vSeSGHaWXR3HpG4/N3M1Z6Ysf7oG5lb+QKitPO0D05tA3vrEn9mw0RPYF3qYhIQbtOzUi3dfD6RLhg+g2ZKdXsfHfnXpP3cTRqNGx9rl8XEtxoyNYVTxKEGzyl4M9q3D2BW7WbjzOCjFmFcaoJQi9EIcs7aFY6fXoVMwwq8exQs7PvqH5rK82AcGg4FhQz7nrxVz0et1zJ/3B8cjTvHpyEEcPHiE1cEbmTd3MTN/mcShw5u4fj2Rfn1MA74GDevy4eB3SE5JwWg0MnjQKOKvXafq85X4v5nfotfr0ekUfy0NZs2aTVbLP2jQZwStXIBer2fO3MVERJxk1KghHDxwmKBV65k9ZxGzZ00m/Nh24uMTCOyd9rV/J07sxKloURwc7PH3b0t7v57Ex19n6Z+zKFDAAb1ex5YtO5n5c84+ORoMBj4YNJJVq35Dr9MxZ+5iwsNPMnr0UA4cCCMoaD2zZi9izpypRISHcP16Aj17mT5rHh5+kj/+XMnhsM2kGAwM/ODTLNeYP6CUYtavk3FyKgJKceRwOO8NGPHE+QcN+oxVQQvR6XXMnbOY8IiTjB41lAMHTflnz17EnNlTCA8P4Xp8Ar0CzfkjTvLnnysJC9uEIcXABx+MTM2/eNFMSpQoTnJyCgM/+JSEhLRvbAro1oHFS5Y/Ue5HMRqMLB01m7fnfYJOr2PPks3EnorE98NuXDpylmMbDtBhRE8KFCpA3xmmrwi9HnWVX9+ciN7ejvf/+ByApFt3WfDhNJsvazEajKwYNYfX541Ap9exb8kWLlBQBTIAACAASURBVJ+KpPWHXYk8co6IDQdo2KcNFRpVw5CSwt3E2ywZ8qPNM88f9QvD5n2GTq9j25JNRJ26ROcPe3D+yGlCN+ynx4jeFCjkyHszhgAQH3WVyW9OsGnu9PJ7DZrByNERc6i/aARKryPy9y3cOhFJxeFdSQg7R9zaA48+ichXlLW+tSGnKKW8gSBN0543359jvv/ng33An8AtTdMmmtscBfw0TTuvlPoN01r11fD/7N13WBTHH8fx93CCJRGjJlGKJbZEYxfsIliwAGI3iRo1iTExxtij0diixjS7scReEnsF7IqIFVRQRMWuFCvF/KyI+/vjzoOjKAmcgPm+nofHu9vZ5TN3t7PD7OyKNzBI0zR3Q7kZQKCmaYvS+v05fVrLk90vf5QiM+Vq1CWrI2SKwiWaZHWEf+1RQvyLC2Vzz+ss5wQ5/br1r2zqZ3WEDLPKpnedSq/rPM7qCP95HR9YZXWEDHO78Ve22hHuz/rarH20fF9Oz5L6ZvuRc03TLgMVkzzvntayJK8nLf9RssW+SZb1ybSgQgghhBBCZFC275wLIYQQQgiRQjruqJIT5ZQLQoUQQgghhHjlyci5EEIIIYTIeXL49URpkZFzIYQQQgghsgkZORdCCCGEEDmPjJwLIYQQQgghzElGzoUQQgghRM6Tzf+vnn9LRs6FEEIIIYTIJmTkXAghhBBC5Dwy51wIIYQQQghhTjJyLoQQQgghch75H0KFEEIIIYQQ5iQj50IIIYQQIufRXs0559I5F0IIIYQQOc8rOq1FOucvULhEk6yOkCH34x9ldYQMeRDZJasjZIo7V3ZmdYQMKV3OM6sjZEhj63JZHSFDlkUeyuoIGTLr+oGsjpBhOoucPQtUobI6QoY8fPI4qyNkmHee17I6QobdzuoA/xHSORfZWk7/4whyfsdcCCGEyI40uZWiEEIIIYQQwpxk5FwIIYQQQuQ8r+iccxk5F0IIIYQQIpuQkXMhhBBCCJHzvKK3UpSRcyGEEEIIIbIJGTkXQgghhBA5j8w5F0IIIYQQQpiTjJwLIYQQQoicR+5zLoQQQgghhDAnGTkXQgghhBA5j8w5F0IIIYQQQpiTjJwLIYQQQoicR+5zLoQQQgghhDAnGTkXQgghhBA5j8w5F0IIIYQQQpiTdM4zWZOmThw9vpOgE7vpP/CLFMutrKxYuHgaQSd2s9t3HcWL2wFQo0Zl/A964X/Qi/2HvHH3cDVZz8LCgn0HNrNqzbxMydnM1ZlTIX6cCfVnyOCvUs355/JZnAn154D/ZkqUsDcu+3ZIH86E+nMqxA/Xpg1fuM0li6dzKsSPoOO7+GPub+TKpT9h8+67pfH328S9vy8yoH+vTKkXZP5nkDu3FXv2rmf/IW8OB2zlu+H9Mi1rRoyYMAkntw9o3SVlHbNSw8b12HN4E36B3vT+5tMUy62sLJk5/xf8Ar3ZuGM59sVsAWjd3o0te1cbfy7fDqZCxXcBsLTMxcTJo/A9spndhzbRwqPJS6tPxYZVmbBrGhN9Z9DyyzYplrt+6sG4HVMYu2USg5ePorDdW8Zl8y+sYozPr4zx+ZW+fww1a05XV2dCQvw4HerP4DT26eXLZ3E61J/9yfbpIUP6cDrUn5AQP5om2acLFLBmxYq5nDy5lxMnfKldq4bJNvv370X84wgKFy6YqXVp2rQhJ07s4dQpPwYN6p1qXZYuncmpU374+W001qVQoTfYtm0Ft2+fZvLksSbrtG/vQUDANo4d28n48d9lat606nA8aBcnTvoycOCXqdZh8ZIZnDjpi+/eDRQvrq9Do0b18d+/mSNHtuK/fzMNG9YxrmNpacn0GRMICt7NseO78PRsbrb8TZo6cSxoF8En9zAgjXZ08ZLpBJ/cw5696xPbUYcqHDjkzYFD3hw85INHq8Rj2e+zf+LS5QCOBGzNtJwv81jmu3sdgQHbCQzYztXLR1m7Zj4AHh6uHDu6g8CA7Rw66EO9uo6ZUrdGTRpw6OhWjgTtoG//z1OpmyXzFk7hSNAOtu1eTTHDZ/CMnb0NlyOP89XXnxhf+/zLj9l3yAv/w9706t0tU3JmJe3pU7P+ZJUs65wrpUoqpUL+QflFSqn2hsfzlFIVUinTXSk1IzNz/hMWFhb8NmkM7dr0wLFGM9p38ODd98qYlPm4W0diY+9StXIjZs5YwJgfvgUgNDSMhvU9qV/HnbatuzN1+jh0Op1xvS+/6kHY2QuZlnPa1PG4e3ShUhUXOnVqTfnyZU3KfNLjQ2Ji4nivQn2mTPuDHycMB6B8+bJ07OhJ5aqNcHPvzPRpE7CwsHjuNv/6az3vV3SiarXG5M2bh08/+QiA6OhY+vX/nkmT52RKvZ7VLbM/g0ePHuPesjP1artRr447TZo64ehYNdMy/1utWzZl9qRxWR3DhIWFBeN+Hk63jr1pXMeTVu1aUPbdUiZlOnVpS1zsXZwc3Jg3aynDRvcHYMMab1o07ECLhh3o98V3hF+NJDTkLABfD/yc27eica7pQeM6nhzaH/hS6qMsLOg6tieTu49neNN+1GpVH9sy9iZlroZeYqzHEEa2GEDglkN0HNbVuOzxw8eMajmIUS0HMa3nRLPlfLb/eXh0oXIVFz5IY5+OjYmjfIX6TJ32BxOS7NOdOnpSpWoj3JPs0wCTJ41l+7Y9VKrUkBo1mnL6zDnj9uztbWnS2IkrV8IzvS5Tp47D07MbVas2pmPHVrz3nmldunfvRGxsHO+/78T06fMYN24YAA8fPmLMmN8YOnS8SflChd7gxx+/o0WLD6levQlFiryJi0u9TM2dvA6TJo+lTevu1KjelA4dWvFesnaoW/eOxMbGUbmSMzOmz+eHcfo/3u7ciaF9+0+pWbM5n/ccyLz5k43rDPm2D7du3aFqlUbUqN4Ef//DZs3ftnV3HKq7Pjd/lUouzEySP/TUWRrUa0Xd2m60bt2NadPGG49ly5eupXXr7pma82Uey5wbtcXB0RUHR1cOHT7K+g1bANi925/qNZri4OhKz88HMmfOr5lSt59+G0Wndj2p59iStu3dKfduaZMynT/uQGxsHDWrNmX2zEWMGjPYZPm4H79j1w4/4/P3ypela7eOuLq0p2HdVrg2c6FU6RIZzioyX44cOdc07TNN00KzOkdyDg5VuHjxCpcvXyM+Pp61a7xwc29qUsbNvQl/LV8LwIb1W3B2rgvAgwcPSUhIACBP7txoSaZR2doWpVlzFxYvWpkpOWs6VuPChctcunSV+Ph4Vq3aSCuPZiZlWnm4snTpagDWrvWmkUt9w+vNWLVqI48fP+by5WtcuHCZmo7VnrvNLVt3G7cbEBCEvb0NALdu3SHwaDDx8fGZUi8w32dw7959QD+Cm8syF5qW9fPcHKpWooB1/qyOYaJqjUpcvnSVq1fCiY9/wuZ1W3Bt4WJSxrWlC2tWbALAZ+MO6jnVSrEdz3Yt2LjWx/i8Y+c2zJyiP2ukaRox0bFmrEWiUlXLcPPKdW5du0FC/BOObPanmqvpqNiZgyE8fvgYgAvHwyhYtPBLyZZU8v1v5aqNeCTbpz3S2Kc9PJqxMpV9On/+16lfvxYLFv4FQHx8PHFxd43b+/XX0Qz7bnym7wuOjlVN6rJ69WY8kp1J9PBwZdmyNQCsW+dj7Gjfv/+AAwcCePTooUn5d94pzrlzl7h9OxrQd6Zat26RqbmTcnCoysULie3QmjWbcXc3rYO7myvLl+nbofXrfYztUHDwKa5H3QT0Awa5c+fGysoKgI8/7sCvv/wO6PeDO3dizJS/Sor8KdpRt6ZJ8qevHd2//0im7rsv+1j2zOuvv4aLcz02btSfAXh2fAB4LV++TNknqjtU5tLFK1wxfAbr13rTws30jGELt8as+Gs9AJs2bKWBc50ky5pw5fI1zp45b3yt3LulORoQbPyMDuw/kuJzzXGeaub9ySJZ3TnXKaX+UEqdUkptV0rlVUpVVUodUkqdUEqtV0qlOF+qlPJVSjkYHvdQSoUppfYC9ZKU8VBKHVZKHVdK7VRKFVFKWSilziml3jKUsVBKnVdKvZkZlbGxLUp4eJTxeWREFLY2RZKVKWIsk5CQwN27f1PIcErYwaEKhwO2cvDIFvr1HWFs4Cb+/D0jh0/kaSadYrG1K8q18Ejj8/CIKGxti6ZZJiEhgbi4uxQuXBBb21TWtSuarm3mypWLzp3bsW3bnkypR2rM9RlYWFjgf9CLC5cD2LN7P4GBwWarQ05W1OZtIiOuG59HRd6gSLL3P2mZhIQE/r77PwoWesOkjEeb5mxcpx+Vsjb8ATLouz5471nJrIW/8eZbL6cDXLBIIaIjbxufR0dFU7BI2r/bqWNjTvoeMz63zG3FyE0/MWL9j1RzrWm2nLZ2RQlPsv9FRERhl8592s425bq2dkUpVaoEt2/fYf68yQQc2cac2b+QL19eANzdmxIZEcWJE5k/RmKbWh7bImmWebYPP29qzYULVyhXrjQlStij0+nw8HDF3t4207Mn5itCeIRpHWxS1CGxTFp1aN26BSeCT/H48WMKFLAGYOTIgew/4MXSZTN5++1MOXSlkr8o4RGJ7WhExPWUxwjbIsYyCQkJxCXJ7+BYlYDAbRwO2Mo33ww3tqOZnjOLjmWtW7dg9579/P33/4yveXo2J+TkXjZtXEzPngMzXDcbmyJEhie2pZGR11N8h2xsihCR/FhWqCD58uWlb/+e/DLRdCLB6dBz1KnnQMFCb5A3bx6auDbE1jBYlmNJ59wsygIzNU17H4gF2gFLgG81TasMnARGpbWyUsoGGIO+U94USDrVxR+orWlaNWAFMETTtKfAMqCzoUwTIFjTtNtJ1kMp9blSKlApFfj4yV3SS6mUryX/C1qRaiEAAgODqeXYHGen1gwc9CW5c1vRvHkjbt+6Q1BQumcApSNnygwpcqZaJu1107PNGdMnsG/fYfz3H/mnkdPNHJ8BwNOnT6lfx53y5epSo0Zlylcol+nZXwX//ruVWKZqjUo8ePCQsNP6ER9dLh22dkUJPHwcN5dOHA0IZsTYjB/80iUd9XmmTmsnSlYuzZa5G42vDarbi7GtvmVO3yl8NLIHbxUvkuq6GY+Z+ft0Lp2OatUqMWfOEhxrNuPevfsMGdKHvHnzMGxoX0aPyfip+9RkxncoudjYOPr2Hc7SpTPZtWsNV66E8+TJk4yHTUO68r2gTPnyZflh3FC+/lo/Pz5XLh329rYcPBhIvbruHDl8jAkTzDN3PqOfQWBAEI4OzWjYwJOBg3ob29HskzNjx7IPOnqyYuUGk9c2btxKxUoNadf+U8aMNp1e8m/867qh8e13fZk9c5HJiD7AubALTJv8B2s3LGTVuvmcOnmGBDPuB+Lfy+rO+SVN04IMj48CpYE3NE3ba3htMeD0nPVrAb6apt3SNO0xkHTehz2wTSl1EhgMvG94fQHwseHxJ8DC5BvVNG2upmkOmqY5WOWyTndlIiOuG6dsANja2RB1/aZpmcjEMjqdDmvr/EQnO80XdvYC9+7dp0KFd6lVpwYt3BpzMtSPhYun4dSwDn/Mn5TuTKmJCI+iWJJRI3s7G6KibqRZRqfTUaCANdHRMUREpLJu5I0XbvP7Ef15663CDBo8OkPZX8Qcn0FScXF/47/vME2aPu9r+d8VFXkDW7vEUSYb2yLcTPb+Jy2j0+nIb/06sTFxxuWt2ppOaYmJjuX+vfts9doFgPfGbVSsUt6c1Uj83dfvUMg2cXSykE0hYm9GpyhXoV5l3Pu0Y+pnP/LkceLBLvamftrBrWs3OHPoFCXef8csOSPCo0xGgu3sbIhM5z4dHpFy3ajIG4RHRBEeHsWRgOMArF3nTbWqlShduiQlSxbnaOAOzoUdwt7ehiOHt1GkyFtkhojU8kTdTLNMWvtwcj4+O3Fy8sTZuQ3nzl3k/PnLmZI3NRER17G3M63D9WR1iExSJnkdbO2K8teKOfT8bACXLl0F9HPR7927z6ZN2wD9dJ4qVSuaKX8U9naJ7aidXdGUx4iI68YyOp2OAql8BmfPXuD+vftUeN+0Hc20nFlwLCtUqCCOjtXw8dmVaqZ9/ocpVapEhi+Sjoy8jq19Yltqa1s05Xco8jp2yY5lMdGxVHeowqixgzl2cje9vuxGv0Ff8OnnXQBYvnQNjZza4NGiMzExcVy4cCVDObOc9tS8P1kkqzvnj5I8TgDeSKvgc6Q1XDIdmKFpWiWgF5AHQNO0a8ANpVQj9J37Lf/id6bq6NETlCpdkhIl7LG0tKRde3d8vHealPHx3sWHndsB0LpNC/buPQhgPN0KUKyYLWXLleLK1XDGjPqF8uXqUamCEz269cVv70F6fjogQzkDAoMoU+YdSpYshqWlJR07erLZa7tJmc1e2+natQMA7dq5scd3v/H1jh09sbKyomTJYpQp8w5HAo4/d5uf9PgQ16bOdO7yldnnapvjMyj8ZiEKFNBPrciTJzfOLvU4d/aiWeuRUwUfC+GdUiUoVtwOS8tceLRtwY6tviZldmzxpf0HrQBo6dmUA/sSz6QopXDzdGXzOtO7Oezctpc69fVzves51X5p7/+l4PO8XdKGN+3fRmeZi5oe9Tm+w/Ri1OLvv0O3Cb2Y9tlE/r6TeKYtn/Vr5LLS35no9YL5KVvjPSLPZe7Fk88k3/86dfTEK9k+7ZXGPu3ltZ1OqezTN27cIjw8knLl9BehNWpUn9OnwwgJOYOdfRXKlqtN2XK1CQ+PomatZty4cStT6hIYGGxSlw4dPPDy2pGsLjvo0qU9AG3btsTX98ALt/uWYSrUG28U4PPPu7LQMJfeHI4eDaZ0mcR2qH17D7y9Tevg7bODzl307VCbNi3Zu1dfhwIFrFm3diGjRv7MoUNHTdbx8dmFk1NtAFxc6nEmyQW6mZv/RIr8KdpRn51J8qfVjtpRtlwprmbyRcPPvOxjGUD7du54++zk0aPE7kvp0iWNj6tVrYiVlWWGrwc4fvQkpUqVpLjhM2jTzo2tyf4g2Oqzmw8+1N9BqlXr5uwzfAYezT+ieqVGVK/UiDmzFjPl19nMn7sMgDffLATo7+Ti3sqVdWu8MpRTmEd2+0+I4oAYpVQDTdP2AV2Bvc8pfxiYqpQqDNwFOgDPJgMXACIMj5PfL2ge+uktSzVNy7TJcAkJCQweOJr1Gxej01mwdMlqzpw+x/AR/Th27CRbfHaxZPFK5s6bRNCJ3cTExNGjW18A6tR1oP+AL4h/8oSnT58yoN9Ios10sU9CQgLf9BuBj/ef6CwsWLR4JaGhYYweNYjAo8F4ee1gwcIVLF40jTOh/sTExPJRF/3tzEJDw1izZjMng/fwJCGBvt8MN86FT22bAL/PnMiVK+H479NfBLhhgw/jxk+hSJG3OHxwC9bWr/P06VP6ft2TSlWcTebx/Zu6ZfZn8H7F95g99xd0Oh0WFor1a33YmuQi16wyeNREAo6fIDb2Lo1bd6H3p11pl+zCpZctISGB74dMYOma2eh0OlYuX0/YmQsMGPYVJ4+fYsdWX1YuW8eU2T/iF+hNbEwcfT4bYly/Vt0aREVeT3Ew/3H0ZKbM/pFRE74l+nY0A/t8/1Lq8zThKctHzmPgku+x0Fmwb9VuIs9do3X/D7h88jxBOwPpOOxjcufLQ+/f9VNt7kTcZlrPidiWsafbhF481TQslMJ71noiz5unk/Jsn/ZOtv+NGjWIo0n26UWLpnHasE93TrJPr16zmROp7NP9+n/PksXTsbKy5OKlq3z2WcYGBtJbl379vmfz5qXodDoWL17J6dNhjBw5gKNHT+LtvYNFi1ayYMEUTp3yIzo6lo8/7mNc/+zZ/eTPnx8rK0s8PJrh7t6FM2fO8dtvo6lUST/zccKEKZw/f8msdRg4YCQbNy1Bp9OxZMkqTp8+x4jv+3Ps2El8vHeyeNEq5s2fxImTvsTExNLt468B6PXFx5QqXYKhw/oydJi+bWrl0ZVbt+7w/YiJzJs/iZ9/Hsnt29H06pXx6RNp5x/Fhk1LjO1oyvwrmTd/MsEn9xATE0d3Q/46dR0ZODCxHe3f73tjR3Xhoqk0cKpN4cIFOXvuAOPHTWHJ4lUZyvkyj2UAnTq24udfZprkaNumJV26tCc+/gkPHzzko84pb535b+o2dPBYVq+fj4VOx59L13D2zHmGDu9L0LEQtm7ZzfIlq/l97i8cCdpBbEwcPXv0f+F2Fy6bQaFCbxAf/4QhA8cQF5v+qbvZ0iv6nxCprLrrhFKqJOClaVpFw/NBwOvABmA2kA+4CPTQNC1GKbXIUH6NUsoXGKRpWqBSqgcwDIgCggCdpml9lFKewGT0HfRDgKOmac6G32UJ3AFqapp25nk5rV8rlaM/+fvxj15cKBvLZ5k7qyNk2J0rO19cKJsrXc4zqyNkSGPrnH2NwLLIQ1kdIUN0FroXF8rmdBZZfaI5Y1K91iYHefjkcVZHyLA38ryW1REy7PbdsGz1RfrfgFZm7aO9PmlTltQ3y0bONU27DFRM8jzp1UW1UynfPclj5ySPF5L6vPGNwMbkrxtUQX8h6HM75kIIIYQQInvSXtGR8+w2rcXslFJDgS9JvGOLEEIIIYQQ2cJ/rnOuadpEwHz/VZ8QQgghhDC/V3TkPGdPohNCCCGEEOIV8p8bORdCCCGEEK+ATPqf07MbGTkXQgghhBAim5CRcyGEEEIIkfPInHMhhBBCCCGEOcnIuRBCCCGEyHlk5FwIIYQQQghhTjJyLoQQQgghchxNk5FzIYQQQgghhBnJyLkQQgghhMh5ZM65EEIIIYQQwpxk5FwIIYQQQuQ8r+jIuXTOX+Dhk8dZHSFDLJTK6ggZ8ighPqsjCOBC2MasjpAh1sVcsjpChhTI81pWR8iQhFf0v9jOSfLkssrqCBnyulWerI6QYXfu383qCCKHkM65EGZWupxnVkfIkJzeMRdCCPFq0mTkXAghhBBCiGziFe2cywWhQgghhBBCZBMyci6EEEIIIXKeV/RyFhk5F0IIIYQQIpuQkXMhhBBCCJHjvKoXhMrIuRBCCCGEENmEjJwLIYQQQoicR0bOhRBCCCGEEOYkI+dCCCGEECLnkbu1CCGEEEIIIcxJRs6FEEIIIUSOI3drEUIIIYQQQpiVjJwLIYQQQoicR+acCyGEEEIIIcxJRs6FEEIIIUSOI3PORbq4ujoTcnIvoaH+DB70VYrlVlZWLF/2O6Gh/vjv20yJEvYAFCr0Btu3rSL6zlmmTBlnLJ83bx42bFjMyRO+BB3fxfhxwyT/i+rQ1JmTJ3wJPbWPQYN6p1qHZUt/J/TUPvb5bTKpw7ZtK7lz+wxTJv9gss7mTUsJOLKN48d2MmP6BCwszLfrNGxcjz2HN+EX6E3vbz5NJb8lM+f/gl+gNxt3LMe+mC0Ardu7sWXvauPP5dvBVKj4LgCWlrmYOHkUvkc2s/vQJlp4NDFb/n9ixIRJOLl9QOsuX2R1lDQ1bdqQ4ODdhITsZdCgL1Mst7KyYunSGYSE7MXPbwPFi+u/T40a1Wf/fi8CAraxf78XDRvWfWmZGzVpwKGjWzkStIO+/T9PJbMl8xZO4UjQDrbtXk2x4nYmy+3sbbgceZyvvv4EgDJl3mGP/0bjz6XwY/Tq3c1s+Rs3ceLIse0cDd5FvwG9UslvxfzFUzkavIsde9YY81evURm/A5vwO7CJfQc34+bR1LhOr97dOHDEhwMBW/iid3ezZc9oHZ6xt7fh2vVg+vTVtwG5c1ux03ct+w5u5kDAFoYO/8as+V0a12dfgDcHjm2lT7/PUslvyewFv3Hg2Fa8d67AvritcVn598uxefuf+B7cxO79G8id2woAS0tLfpkyGv9AH/Yd8cKtVdMU283M/P4BPhx8Tv45CyZx8NhWfHauoFiy/F7b/2Lvwc3s2b+R3LmtyJs3D8tWzmbfEW/2HtzM8FEDzJLb1dWZkBA/Tof6M3hwGsfg5bM4HerPfv/EYzDAkCF9OB3qT0iIH02bNjRZz8LCgoAj29iwfnGKbU6Z/AMx0WGZXxnxr+WIzrlSylcp5fCCMt2VUjNeVqbUWFhYMHXqODxadaVKFRc6dfKk/HtlTcr06PEBMbFxVKhQn2nT/mDC+O8AePjwEaPH/MK3Q39Isd3Jk+dQqbIzjjWbU6eOA82auUj+F9ShlefHVKnaiE4dPXkveR26f0BsbCwV3m/AtOnzGD8usQ5jxvzK0KHjUmz3o85f4lizGdWqN+HNNwvTrp272fKP+3k43Tr2pnEdT1q1a0HZd0uZlOnUpS1xsXdxcnBj3qylDBvdH4ANa7xp0bADLRp2oN8X3xF+NZLQkLMAfD3wc27fisa5pgeN63hyaH+gWfL/U61bNmX2pJTvd3ZhYWHBlCk/4OnZjWrVmtChQ6sU36fu3TsRExNHxYoNmT59PuPHDwXgzp0Y2rf/BEfHZvTsOYAFCya/tMw//TaKTu16Us+xJW3bu1Pu3dImZTp/3IHY2DhqVm3K7JmLGDVmsMnycT9+x64dfsbn589fwqW+Jy71PWns1Ib7Dx7gvXmH2fL/Mmk0Hdp+Sm2H5rTr4M6775UxKdO1WwfiYuOoUaUxs2YuZPQPQwA4HRqGS4M2ONVtRfvWnzB52jh0Oh3lK5SlW/dONG7Ylga13WnWwoVSpUuYJX9G6/DM+J+GszPJZ/Do0WM83brSoI4HTnU8aNykAQ6OVc2Wf8KvI+jcvhcNa3nQun3LFN+hD7u2Iy72LnWrN2fu74sZMXogADqdjhlzf+LbAWNwrtOKdu7diI9/AsA3g3px+1Y09R1a4lTLg4P+AWbL/+Ov3/NR+89xquVBm/ZuKfJ/1LU9sbFx1KnenDm/L2HE6EHG/DPn/syQAaNpWMeDtknyz5qxgAY13Wji1BbHWtVo1KRBpueeNnU8Hh5dqFzFhQ86taZ8edP25pMeHxIbE0f5CvWZOu0PJkwYDkD5SmZswAAAIABJREFU8mXp1NGTKlUb4e7emenTTAeR+n79GafPnEvxO2tUr8wbbxTI1Hq8VE/N/JNFckTnPKdwdKzKhQuXuXTpKvHx8axatREPD1eTMh4erixduhqAteu8cXGpD8D9+w84cCCAhw8fmZR/8OAhe/ceACA+Pp7jQSHY2dlI/vTWYfWm1OuwbA0A69Z54+JSz7QOjx6l2O7ff/8PgFy5cmFlZYmmmedUWtUalbh86SpXr4QTH/+Ezeu24NrC9I8Z15YurFmxCQCfjTuo51QrxXY827Vg41of4/OOndswc8o8ADRNIyY61iz5/ymHqpUoYJ0/q2Ok6dn36fLla8THx7N69Wbc3U1H+9zdm7J8+VoA1q3zwdlZ/30KDj5FVNRNAEJDw8idOzdWVlZmz1zdoTKXLl7hiiHz+rXetHAzPVPSwq0xK/5aD8CmDVtp4FwnybImXLl8jbNnzqe6fSfnOly+dJXwa5FmyV/DoQoXk+Rft8ablinyN+Gv5fr8G9dvpaEh/4MHD0lISAAgd57cxv203LtlCDgSZFy+3/8I7snahexSB4CW7k24cukaZ06bdqbu3bsP6M+EWVqarx2qVqMSly8+a4fi2bh2C81aNjIp07xlI1b9tQEAr43badCwNgANG9XjdEiYcWAgJiaOp0/1vZwPOrdh2uQ/AH07FG2mdqhajcpcSpJ/w1qfFPmbtWzEqr82GvJvo74hv3OjeoSGnE2SP5anT5/y4MFD9u87AuiPZSdPhGJjWzRTc9d0rGZy/Fq5aiMeHs1Mypgcg9d608hwDPbwaMbKVRt5/Pgxly9f48KFy9R0rAaAnZ0NLVo0ZsGCv0y2ZWFhwcSJ3zN0WPYdIPmvMkvnXCk1RCnV1/B4slJqt+FxY6XUMqWUq1LqoFLqmFJqtVLqdcPyGkqpvUqpo0qpbUopm2TbtVBKLVZKjTM876GUClNK7QXqJSnnoZQ6rJQ6rpTaqZQqYlj3nFLqrSTbOq+UejOz6m1na0P4tSjj84iI69gm64ja2RYlPFxfJiEhgbi7dylcuGC6tl+ggDVubk3Ys8c/syIny5az8wPY2hblWnhipyEiIgq7ZA2orW1Rwg1lEhISuHv373TVwWvzMsKvHefv/91j3TrvzA1uUNTmbSIjrhufR0XeoIhNkTTLJCQk8Pfd/1Gw0BsmZTzaNGfjui0AWBs6v4O+64P3npXMWvgbb75V2Cz5XzW2Sb7vYPg+2f3z71ObNi0JDj7F48ePzZ7ZxqYIkeGJ36HIyOvY2BZJUSYiyX589+7fFCpUkHz58tK3f09+mZj2Scg27dxYt8Y8338AG9vEbACRESnz29omyx/3PwoZ3vMaDlU4ELCF/Ye9GfDN9yQkJHA6NIy69RwpWOgN8ubNQ1NXZ+zszTdIkJE65MuXl2/69+KnH6en2K6FhQV+BzYRdukwvrv9ORoYbJb8RW2KEGHSDl2nqM3bKcokbYf036E3KF2mBBoaf62dy/a9a+jdVz81yrqAvh36dvjXbN+7hrmLJputHbJJpR21sUm5D0RGJL7/fxvylypTEg34a+0fbN+7lq/6ppxaaF0gP67NXdi392Cm5ra1S2xLII3jl13iMS4hIYG4OP0x2M425bq2hrbqt9/GMGzYOOMfSc981bsHXl7buX79ZqbW42XSnpr3J6uYa+TcD3h2vscBeF0pZQnUB04CI4AmmqZVBwKBAYbl04H2mqbVABYA45NsMxewHAjTNG2EoeM+Bn2nvClQIUlZf6C2pmnVgBXAEE3TngLLgM6GMk2AYE3TbicPr5T6XCkVqJQKfJpwL92VVirla8lHNlQqhdIz+qHT6Vi6dCYzZy7g0qWr6c70T+T0/JC+fOmpZ2rcPbpQoqQDua2sjKPtmS19+Z9fpmqNSjx48JCw0/qRT10uHbZ2RQk8fBw3l04cDQhmxNiBmZz81ZQZ+0T58mUZN24offqY/3qL9ORJswwa337Xl9kzFxlHaJOztLSkecvGbFq/JXPCpiJdbcxzyhwNDKauYwsaN2xL/4FfkDu3FWFnLzB18lzWb1rMmg0LOBVymidPEsySXx/v39dh6PBvmDVzYaqfwdOnT3Gq24r3361PdYcqlK9QNkWZzJD69yMdZTQNnS4XNWtX56ueQ/Bs3oUW7k2o71SbXDoddvY2BBw+jmvD9hwNCGLUuMEptmG+/OnZbyGXTket2tX5qudgPJt3NuZ/RqfTMXver8ybs4yrV8LNnztd7U3a67Zs2YRbN29z7PhJk2U2NkVo186dGTMXZDC1MAdzdc6PAjWUUvmBR8BB9J30BsAD9B3p/UqpIKAbUAJ4F6gI7DC8PgKwT7LNOUCIpmnPOuy1AF9N025pmvYYWJmkrD2wTSl1EhgMvG94fQHwseHxJ8DC1MJrmjZX0zQHTdMcLHSvpbvS4RFR2BdLHI2xsytKVOT1lGUMIzY6nY4C1tbpOrU36/efOH/+EtOnz093nn8qp+cH/WhBMfvEC3vs7GyIjLqRrMx17A1ldDod1tb503169dGjR3h578DD3TynxKMibxhHO0A/Ancz2ahG0jI6nY781q8TGxNnXN6qremUlpjoWO7fu89Wr10AeG/cRsUq5c2S/1Wj/64k3SdsiIxM/n2KSvP7ZGdXlJUr5/LZZwPM+kdpUpGR17G1T/wO2doW5XrUzRRl7JLsx9bW+YmJjqW6QxVGjR3MsZO76fVlN/oN+oJPP+9iXK9JUydOBJ/i1q075ssfcd1kVNvWLpX8EcnyF3g9xVStsLMXuH//AeUrlANg2ZLVONf3xK3ZR8REx3HxwuVsWQcHxyqM+WEIwad8+bJ3dwYM+pKevbqarHs37m/89x2mcRMns+SPirxucobIxrYoN6KSt0PXTdoha+v8xMTEERV5nYP7A4iOjuXBg4fs3uFHpSoViDa0Qz6bdwKwecM2KlWugDlEptKOprYPPDszrG9H8xMTE0tk5A2T/Lt2+FG5SmLOX6eO4eLFK/wxa0mm544IT2xLII3jV3jiMU6n01GggDXR0TGGY7PpulGRN6hb1wF3d1fOhR1i+bLfcXGpx+JF06hatSKlS5fkzOn9nAs7RL58eTkdar6z2mYjc87TT9O0eOAy0AM4AOwDXIDSwCVgh6ZpVQ0/FTRN+xRQwKkkr1fSNC1pD+gA4KKUypP0V6URYTowQ9O0SkAvII8h1zXghlKqEfrOfaYO/wQGBlOmzDuULFkMS0tLOnb0xMvL9KIpL68ddO3aAYB2bd3w9d3/wu2OGT2YAgWsGThwVGbGTSGn54dndSiZWIcOrVKvQ5f2ALRNRx1eey0fRYvqT+nqdDqaN2vE2bOpz8fNqOBjIbxTqgTFitthaZkLj7Yt2LHV16TMji2+tP+gFQAtPZtywDAPEvSjJ26ermxet9VknZ3b9lKnviMA9Zxqc+7sRbPkf9U82ydKlNB/nzp08MDb2/T75O29k86d2wHQtm1L4zUWBQpYs27dQkaO/JmDB1/eBbjHj56kVKmSFC9hj6WlJW3aubHVZ5dJma0+u/ngwzYAtGrd3Hh63qP5R1Sv1IjqlRoxZ9Zipvw6m/lzlxnXa9vBnXWrvcya/9jRE5QuXcKYv217N7akyL+LDzvr83u2aY7f3kMAFC9hj06nA6BYMVvKlH2Hq1cjAHjzrUKA/i4o7p6urFm9OVvWoaXrh1R535kq7zsz6/dFTPp1Fn/MWUrhNwsZp4bkyZMbZ5e6nAszz34cdCyEd0qXoFgJOywtLfFs14JtW/aYlNm2ZQ8dP2wNgLunK/5+hwHw3bWfCu+/S968edDpdNSu50iYob3cvtWXug1qAlC/YW3Czl4wU/6TlCpdguKG/K3btWR7svzbt+yh44eehvzN2O93yJDfn/JJ8tep52jM+e3wb8hvnZ/vh/5oltwBgUEmx+BOHT3x8tpuUsbLa3viMbidG3sMxy8vr+106uiJlZUVJUsWo0yZdzgScJwRIybyTikHyparTecuvdmzZz/duvdly5ZdFCtejbLlalO2XG3DH7L1zVIvc3pVp7WY8z7nfsAg9CPUJ4FJ6EfUDwEzlVJlNE07r5TKh36k+yzwllKqjqZpBw3TXMppmnbKsL35gBOwWinVBjgMTFVKFQbuAh2AZxPwCgARhsfJ7/c1D/30lqWapmXqec2EhAT69fseb6/lWOgsWLxoJaGnwxg1chBHjwXj5bWDhQtXsGjhVEJD/YmJjqVL18Rb/YWdPYi1dX6srCxp5dEMN7ePuPv3/xg27BvOnDnHkcP6DtfvsxaxcOFfacX4z+ZPWgevzcvQ6XQsWryS06fDGDlyIMeOnsDLewcLF61g4YIphJ7aR3R0LF0/Trxd1dmzB7DOr6+Dh0cz3Nw7Ex0dw9o1C8id2wqdzgJf3wPM/WPZc1JkLP/3QyawdM1sdDodK5evJ+zMBQYM+4qTx0+xY6svK5etY8rsH/EL9CY2Jo4+nyXe5aFW3RpERV5Pcbr1x9GTmTL7R0ZN+Jbo29EM7PO9WfL/U4NHTSTg+AliY+/SuHUXen/alXbJLoDKSgkJCfTvP5LNm5eg0+lYvHgVp0+f4/vvB3Ds2Am8vXeyaNFKFiyYTEjIXmJiYunatQ8AX3zRjdKlSzJ06NcMHfo1AB4eXc066vws89DBY1m9fj4WOh1/Ll3D2TPnGTq8L0HHQti6ZTfLl6zm97m/cCRoB7ExcfTs0f+F282bNw8NXeoy4BvzfncSEhIYMnAMazcsRKfTsXzpas6cPsewEd8QdCyELT67WLp4FbPn/cbR4F3ExMTyafd+ANSp48A3A3vxJD6ep081BvUfRfSdGACWLJ9JwUIFeRIfz+ABo4mLvZst65CWokXe4ve5v6DTWWBhYcH6dT5s27rnuetkJP93g8fz19o/0OksWLFsPWFnzjP4uz4EHz/F9i17+GvpWqbP+YkDx7YSGxPLF5/o73YSF3eXOTMXs2X3KjRNY9cOP3Zt1991ZvzoSUyfM5GxPw7lzu0Y+n813Iz5x/HX2nnodBb8tWwdZ8+cZ8h3XxN0PITtW/bw59I1zJjzEwePbSU2Jo5enwxMkn8RW3evNubfuX0vNrZF6D/4C8LOXmCHn/4C8AVz/+TPpWsyNfc3/Ubg7f0nOgsLFi1eSWhoGKNGDeLoUf0xeMHCFSxaNI3Tof7ExMTSuYv+GBwaGsbqNZs5EbyHJwkJ9P1meIo55iLnUOa62lsp1RjYCryhado9pVQYMFvTtEmGkeufgNyG4iM0TduklKoKTEPfuc4FTNE07Q+llC8wSNO0QKXUGKAc+rnj3YBhQBQQBOg0TeujlPIEJqPvoB8CHDVNczbksgTuADU1TTvzonpY5bZ/Ne9wn0OkNo8upymS740XF8rGLoRtzOoIGWZdzHy373wZXrPM/eJC2ViCdBKyXJ5c5r9TkDklnzOeE925b74/CF+W+McR2eqgfLtZQ7N+Md7ctjdL6mu2znl2Zbhf+mRN09J1g1LpnGct6ZxnPemcZz3pnIuMks551pPOeeZ7VTvn5pzWku0opYYCX5J4xxYhhBBCCJEDZeW8cHP6T/0nRJqmTdQ0rYSmaTnwkmQhhBBCCPGq+0+NnAshhBBCiFeDjJwLIYQQQgghjJRSzZVSZw3/6/zQNMp0VEqFKqVOKaX+fNE2ZeRcCCGEEELkOFk9cq6U0gEz0f9P9eFAgFJqk6ZpoUnKlEV/Z8F6mqbFKKXeftF2ZeRcCCGEEEKIf64mcF7TtIuG/61+BeCZrExPYKamaTEAmqbd5AWkcy6EEEIIIXIeTZn1Ryn1uVIqMMnP58kS2AHXkjwPN7yWVDmgnFJqv1LqkFKq+YuqJdNahBBCCCGESEbTtLnA3OcUSe0+6MnvvZ4LKAs4A/bAPqVURU3TYtPaqHTOhRBCCCFEjpPVc87Rj5QXS/LcHohMpcwhTdPigUtKqbPoO+sBaW1UprUIIYQQQgjxzwUAZZVS7yilrIAPgE3JymwAXACUUm+in+Zy8XkblZFzIYQQQgiR42hPU5tV8hJ/v6Y9UUr1AbYBOmCBpmmnlFJjgUBN0zYZlrkqpUKBBGCwpml3nrdd6ZwLIYQQQgjxL2ia5gP4JHttZJLHGjDA8JMu0jkXQgghhBA5TjaYc24WMudcCCGEEEKIbEJGzl9AfzZCZJVX4f1vbF0uqyP85929tierI2RY5QofZHWEf+3Gg5isjvCfdy/+YVZHyJBHCfFZHSHDcumky5XZNC1r55ybi3xThBDPZV3MJasjZNir0DkXQgjx3yCdcyGEEEIIkeO8qnPOpXMuhBBCCCFynKy+laK5yAWhQgghhBBCZBMyci6EEEIIIXKcV+CeEamSkXMhhBBCCCGyCRk5F0IIIYQQOY7MORdCCCGEEEKYlYycCyGEEEKIHEdGzoUQQgghhBBmJSPnQgghhBAix5G7tQghhBBCCCHMSkbOhRBCCCFEjiNzzoUQQgghhBBmJSPnQgghhBAix9E0GTkXaXB1dSYkxI/Tof4MHvxViuVWVlYsXz6L06H+7PffTIkS9sZlQ4b04XSoPyEhfjRt2tBkPQsLCwKObGPD+sUmr48d+y2nTu3jxAlf+nz1SbbLX65caQIDtht/7tw+Q9+vPwOgXTt3goJ28+jhNWpUr5zh7ObI/0xq7//cOb9yNHAHx47uYMWKubz2Wr5MqUNaKjasyoRd05joO4OWX7ZJsdz1Uw/G7ZjC2C2TGLx8FIXt3jIum39hFWN8fmWMz6/0/WOoWXOmpWnThgQH7yYkZC+DBn2ZYrmVlRVLl84gJGQvfn4bKF5c/9k0alSf/fu9CAjYxv79XjRsWPdlR0+XERMm4eT2Aa27fJHVUdJU36U2PgdWs/XwWj77+uMUyx1qV2PtziWcjDyAq3sjk2VzV0zl8LldzFo26WXFBaBxkwYcPraNwKCdfDPg8xTLraysmL9oCoFBO9mxew3FituZLLezt+FqVBB9+n4KQJmy77B3/ybjz5WI43zRu3uOqgOAdYH8LFo6nUNHt3IocCuONauaLX+Tpk4cPb6ToBO76T8w5ffbysqKhYunEXRiN7t911HckL9Gjcr4H/TC/6AX+w954+7harKehYUF+w5sZtWaeWbLDuDa1JmTJ3wJPbWPQYN6p5p/2dLfCT21j31+m4zHhUKF3mDbtpXcuX2GKZN/MJbPmzcPG9Yv4kTwHo4f28m4H8zfppqj/Rw9ejDnzh3k1q1Qs+cX/95L65wrpS4rpd5M5fUD5v4d5mRhYcG0qePx8OhC5SoufNCpNeXLlzUp80mPD4mNiaN8hfpMnfYHEyYMB6B8+bJ06uhJlaqNcHfvzPRpE7CwSPxI+n79GafPnDPZVrePO1LM3paKFZ2oXNmZlas2Zrv8YWEXcHB0xcHRlZq1mnP//gM2bNwCwKlTZ+jYsSf79h3KUG5z5n8mtfd/4KDR1HBoSvUaTbl2NYLevXtkSj1Soyws6Dq2J5O7j2d4037UalUf2zL2JmWuhl5irMcQRrYYQOCWQ3Qc1tW47PHDx4xqOYhRLQcxredEs+VMi4WFBVOm/ICnZzeqVWtChw6teO8908+me/dOxMTEUbFiQ6ZPn8/48foD3p07MbRv/wmOjs3o2XMACxZMfun506N1y6bMnjQuq2OkycLCgu9/GsLnH36DR/1OuLVtRuly75iUiYy4zrC+Y/Fetz3F+gtmLuPbr0a9rLiAPvPPv42mY9vPqOPYgnbt3Xn33TImZbp83J7Y2Ls4VG3CrJkLGT12sMnyCROHs2uHn/H5+XOXaFivFQ3rtcKlQWvuP3iA1+aU9c3OdQD48ecR7NrpR+0azWlQx4OzZy+YLf9vk8bQrk0PHGs0o30HD959zzT/x906Eht7l6qVGzFzxgLG/PAtAKGhYTSs70n9Ou60bd2dqdPHodPpjOt9+VUPwsyUO2n+qVPH0crzY6pUbUSnjp4p2p4e3T8gNjaWCu83YNr0eYwf9x0ADx8+YsyYXxk6NOV+PXnKHCpXcaFmrRbUqetIM1dns9bBHO2nj89OGjTwNFvul017at6frPJSOudKKV1ayzRNy55DYulU07EaFy5c5tKlq8THx7Ny1UY8PJqZlPHwcGXp0tUArF3rTSOX+obXm7Fy1UYeP37M5cvXuHDhMjUdqwFgZ2dDixaNWbDgL5Nt9er1MePGT0Yz3D/o1q072TL/M40a1efixStcvRoBwJkz5wkLy7yG+WW//3///T/j47x58xg/B3MoVbUMN69c59a1GyTEP+HIZn+quTqalDlzMITHDx8DcOF4GAWLFjZbnn/K0bEqFy5c5vLla8THx7N69Wbc3ZualHF3b8ry5WsBWLfOB2fnegAEB58iKuomoD/Y586dGysrq5dbgXRwqFqJAtb5szpGmipXf5+rl8IJvxJJfPwTfNZvp1FzJ5MykdeiCAs9z9OnKY9Eh/YFcO9/919WXABqOFTm0sUrXDF8b9at9aaFe2OTMi3dmrDiz3UAbNywFSfnOonL3Jtw+fI1zpw2/cP6mYbOdbl86Srh1yJzVB3y53+dunUdWbpY35bFx8dzN+5vs+R3cKjCxYtXjPvu2jVeuCXbd93cm/CXYd/dsH4Lzs76Q/mDBw9JSEgAIE/u3Ca3urO1LUqz5i4sXrTSLLmfedb2PDsurFq9CY9kI/geHq4sXbYGgHXrvHFx0bc99+8/4MCBAB4+emRS/sGDh+zdexDQv/dBx09iZ29j9jpkdvt55Mhxrl+/abbcInO8sHOulBqilOpreDxZKbXb8LixUmqZUupDpdRJpVSIUuqnJOv9Tyk1Vil1GKiT5PW8SqmtSqmez8oZ/nVWSvkqpdYopc4opZYrpZRhWUvDa/5KqWlKKS/D64WVUtuVUseVUnMAleT3bFBKHVVKnVJKfW547VOl1OQkZXoqpTJ0vtbWrijh4YmNfEREFHa2RVOUuWYok5CQQFzcXQoXLoidbcp1be306/722xiGDRuX4oBZqlRJOnRoxaGDPmzetJQyZUxHwbJL/mc6dfRk5coNGcqYFfnTev8B5v0xifBrQbz7bhlmzlxgjmoBULBIIaIjbxufR0dFU7BI2p1vp46NOel7zPjcMrcVIzf9xIj1P1LNtabZcqbF1rYo4eFRxucREVHYJft+2Cb5DBISErh7928KFy5oUqZNm5YEB5/i8ePH5g/9inm76Ftcj7hhfH4j6iZFbN56zhpZz8amKBERid+byIjr2NgUMS1jW4SI8OuA4XsT9z8KFS5Ivnx5+ab/5/z84/Q0t9+2vRtrV3uZJ/yzfGaoQ4mSxbh9O5oZs3/C138jU2eMJ1++vObJn2zfjYyIwjaV/M/KPNt3Cxn2XQeHKhwO2MrBI1vo13eEsbM+8efvGTl8YqrtamaytU1s8yGN40I62p60FChgjZtbE/bs2Z95oZOR9jN9nmrKrD9ZJT0j535AA8NjB+B1pZQlUB84B/wENAKqAo5KqdaGsq8BIZqm1dI0zd/w2uvAZuBPTdP+SOV3VQP6ARWAUkA9pVQeYA7QQtO0+kDSI8sowF/TtGrAJqB4kmWfaJpWw5C5r1KqMLACaGXID9ADWJiO9yBNhr8fTCQfTU29TNrrtmzZhFs3b3Ps+MkUy3PntuLhw0fUrtOS+Qv+5I+5v2UgvXnyP2NpaYm7uytr1prvQPiy33+Az3oOoHiJ6pw5c46OHVr9y+TpkI66PVOntRMlK5dmy9zEaU6D6vZibKtvmdN3Ch+N7MFbxYukuq65pBI/nZ9NYpny5csybtxQ+vQZlun5/gvS+u5nZxn53gwd3pdZMxZy717qo/2WlpY0b9mIjeu3ZErWtJijDrly6ahS9X0WzvsT5/qe3L/3gH4DemVq7sRsKV9LkZ9UCwEQGBhMLcfmODu1ZuCgL8md24rmzRtx+9YdgoJCzBHZNFu6jgsp10vPmVCdTsfSJTOYOXMhly5d/dcZX0Taz/+29HTOjwI1lFL5gUfAQfQd3gZALOCradotTdOeAMuBZ+dME4C1yba1EVioadqSNH7XEU3TwjVNewoEASWB94CLmqZdMpRJOs/ACVgGoGmaNxCTZFlfpVQwcAgoBpTVNO0esBtwV0q9B1hqmpaiB6aU+lwpFaiUCnz69N7z3hsiwqOwt7c1PrezsyEy6kaKMsUMZXQ6HQUKWBMdHUN4RMp1oyJvULeuA+7urpwLO8TyZb/j4lKPxYumARAeEcX69d4AbNiwhUqVyj8334uYI/8zzZu7cPz4SW7evI25vOz3/5mnT5+yavUm2rRxM1vdYq7foZBt4iUUhWwKEXszOkW5CvUq496nHVM/+5Enj58YX4+9qd8dbl27wZlDpyjxfsbOsvxTERHXsU9y2tfOzobIyGSfTZLPQKfTYW2dn+joWEP5oqxcOZfPPhtg1oPgq+xG1E2K2iX+UVbE5m1uXr+VhYleLDLyOnZ2id8bW7uiKU7DR0Zcx85eP4qo0+mwLvA6MdGx1HCowugfhhAUsocvenen/8Av+OzzLsb1mrg6cSIoNMPTAbOiDpER14mMuM7RwGAANm7cSuWq75snf7J919bOhqjk+SMTyyTfd58JO3uBe/fuU6HCu9SqU4MWbo05GerHwsXTcGpYhz/mm+dC44iIxDYf0jguRFxPs+15nt9//4nz5y8xfcb8zA2djLSf6aNpyqw/WeWFnXNN0+KBy+hHmQ8A+wAXoDTwvE/8oaZpCcle2w+0UKn9uaeXdJJXAvpbPb7o3Unxp65SyhloAtTRNK0KcBzIY1g8D+jOc0bNNU2bq2mag6ZpDhYWrz33lwcEBlGmzDuULFkMS0tLOnX0xMvL9EIjL6/tdO3aAYB27dzY47vf+Hqnjp5YWVlRsmQxypR5hyMBxxkxYiLvlHKgbLnadO7Smz179tOte18ANm3aiothXpmTUx3Onbv4grfn+cwC4ZkhAAAgAElEQVSR/5lOnVqbdUqLufI/7/0vXbqkcbvubk05e/a82ep2Kfg8b5e04U37t9FZ5qKmR32O7wg0KVP8/XfoNqEX0z6byN937hpfz2f9Grms9HdKfb1gfsrWeI/Ic+Fmy5qawMBgypR5hxIl9J9Nhw4eeHvvMCnj7b2Tzp3bAdC2bUv27tVfH16ggDXr1i1k5MifOXgwMMW2RfqcPB5KiVLFsCtui6VlLlq2cWXPtn1ZHeu5jh09SanSJSlewh5LS0vatnNjq/cukzJbfHbxwUdtAfBs3Zx9e/UXmLs1+4iqFV2oWtGF2b8vYvJvs5k3d5lxvXbt3Vm7xrxTWsxVh5s3bxMREUWZsvo/shs2rMPZM+Zpf44ePUGp0iUpYcjfrr07Pt47Tcr4eO/iQ8O+27pNC+N87BIl7I0XgBYrZkvZcqW4cjWcMaN+oXy5elSq4ESPbn3x23uQnp8OMEt+fdtT0nhc6NihFV5epm2Pl9cOunZpD0Dbtm74+r54isro0YMpYJ2fgYNGmyO2CWk//9vSe59zP2AQ8AlwEpiEfkT9EDDFcIeUGOBDIO3JfjAS+B74HUh5X6DUnQFKKaVKapp2GeiULFdnYJxSqgXwbLJVASBG07T7hhHy2s9W0DTt/+zdd3wT9RvA8c83aQtllSF0soeAjCIF2ZuyWigbGYI/FTdThqKCCgi4URRxsJEtsxRK2bJHWQXKKtDN6GDTJvf7IyE0tAyhaRt83q9XXvRy37s8z+USvvfc9y67lFLFgReBp76Xn8FgYOCgj1m9eh56nY4ZMxcQFhbO6NEfsG/fQVatCubP6fOZMWMyx8K2kZCQSK/epts6hYWFs2jxSg4d3EiqwcCAgaMeORZv0qQpzJr5EwMHvsG1azd4861hD22fXfE7O+emRfNGvPPOCKvX69ChNd9/N5aiRQuzfPksDh48Sju/Xjku/owopfjzj+8pUCAfKMXhQ2G8a8PThUaDkbmf/s7QWZ+g0+vYunAD0ScvEDC4BxGHTxG6fi/dPnyFXHly887PQwG4HHWJyW9MwKOcF33Hv4lR09Apxepf/ib6VNZ2zg0GA4MHf8rKlbPQ6/XMnLmQY8dO8sknQ9i//xCrV69nxowF/Pnndxw5spmEhET69HkPgLfe6kvZsqUYOfJ9Ro58HwB//z42r3j+W8NGT2DPgUMkJibTPKA377zWh873XZCcnQwGA2NHfsXvCyaj0+tYOm8lp06c4f0R/TkSeoyNa7dSxbsSP86YRAGXAjT1bcj7w/vj36gHALNXTKNMuZLkyevMxtCVfDx4HP9szJw7LT0s5uEffMbiZX+i1+mZO3sxx4+f4sNRAzlw4DBBgRuYM2sRU3/7mr2h60lISOT1Vwc/cr3Ozrlp0qw+gwd+YtP4bZnDiA++4Nffv8HJyZGIiAu897ZtbudnMBgYNnQMfy+fiV6vY/asRRw/dpJRHw9i//7DrAkMYdbMBUz7/VtCD20gISGJV/uaChh16/kweMhbpKSmYjQaGTLoU65cTnjEK2Z+/IMGfcKqlXPQ6/XMmLmAY8fC+fTToezfd4hVq4OZPmM+0//8nrCjW7lyJZE+r9y7De+JE9spkD8/Tk6O+Pu3op1fL65evcqHIwdw/PhJdu00DYv6ZeoMpk+fb7McbPH9OW7ch3Tv3oE8eZw5dWon06fPZ9y4722SQ1Z4Vn8hVD3OGCulVHMgCCioadp1pVQ4MFXTtG+VUj2BDzFVuAM1TRtuXuaapmn50qwjAtNwmMvAn8BFTdOG321nrnZ/oGman7n9T8BeTdNmKKX8ga+AS8BuwFXTtF7mceR/Ac8Bm4FOQE3gKrAM8AROYBqnPkbTtE3mdY8EvDVN6/Go3B2dPHP4CE2R0/X2qPPoRjnY/Dj7r7wkX9iY3SE8tWqVH/l1lWPF3czazplIL9V4/4ls+3LbkJLdITw1nbL/n5a5efNcjuoNHyvf1qZ9tEonA7Ml38eqnGuaFgI4ppmukObvecC8DJbJd990qTSTr97fztxx3pTm+ffStN+oaVpF83CYKcBec5vLQNr7I6UtPbR5SEoNgJx542QhhBBCCPGfZS+HcW8opUKBo5iGrPz6JCtRShU0V/1vmg84hBBCCCGEHdKMyqaP7PK4Y86zlaZp35EJlW5N0xKBCo9sKIQQQgghRDawi865EEIIIYQQaWXnDwXZkr0MaxFCCCGEEOKZJ5VzIYQQQghhd7Lzh4JsSSrnQgghhBBC5BBSORdCCCGEEHbnMX6qxy5J5VwIIYQQQogcQirnQgghhBDC7sjdWoQQQgghhBA2JZVzIYQQQghhd+RuLUIIIYQQQgibksq5EEIIIYSwO3K3FiGEEEIIIYRNSeVcCCGEEELYnWf1bi3SOX/G5XHKnd0hPJWbKbezO4SnNid6Z3aH8FRccufN7hAEcChsfnaH8FQqVuyS3SE8FZ2y707AxjIu2R3CU2l59lp2h/DUuuYpn90hCDshnXMhxDOvWuUe2R3CU7H3jrkQQtiC3K1FCCGEEEIIYVNSORdCCCGEEHbnWR1zLpVzIYQQQgghcgipnAshhBBCCLvzjN7mXDrnQgghhBDC/siwFiGEEEIIIYRNSeVcCCGEEELYHbmVohBCCCGEEMKmpHIuhBBCCCHsjjG7A7ARqZwLIYQQQgiRQ0jlXAghhBBC2B0NGXMuhBBCCCGEsCGpnAshhBBCCLtjfEZ/hUgq50IIIYQQQuQQ0jnPZL6+TThyZAvHwrYxbNi76eY7OTkxd+4vHAvbxj/bVlKypJdl3vDh73EsbBtHjmyhZcvGAFSoUJa9e9ZZHpcvHWfA+6/bLP7mLRqxd38wBw5uYPCQNzOMf/rMyRw4uIGQjUsoUcITgBdrVmPr9pVs3b6SbTtW4efva1nGxSU/s+b8xJ7969i9by21atfI1Jh9fZtw5PBmwsK2MeyDB2zzOT8TFraNbVvv2+bD3iUsbBtHDm+2bHOA9957jQP71xN6IIT333/N8vzcOT+zZ/da9uxeS/iJHezZvTbzcsjE/QbAxaUA8+dP4/DhzRw6tIk6L9W0WufgwW+ScieKIkUKZUoOdzVr0ZCd+4LYHRrMgMH9M8jFkd+nf8/u0GDWblhEcfM+dJenlzsR0Qd49/3/AVCuXGk2bltueZyN3M+b7/TN1JgfpkHTOgRuX0TQriW8/v4r6eb71KnBkvWzOBy9HV+/Zlbzps3/gV0nQ/hlzrdZFe6/8vH4b2nUrgcBvd/K7lCsNGpWj+CdS9mwezlvDuiXbr6TkyOTf5/Aht3LWbJ2Jp7F3QFwdHRg4uQxBG5ZwKpN83mp/r19fuhH77LtYCCHIrZlSQ4Nm9Vl7Y4lrN+9jP4PyOH7375k/e5lLA6yzmHC5NGs2ryAFRv/onY9Uw558+ZhxcZ5lseu4yGMGjs0S3LJVacWrgtn4rZ4NvlfeTnd/DztWuEetJRis6dRbPY08rRva5nn8l5/XP/6E9f503EZ8l6WxHu/Z+EzXK5xNQaEfMXATd/Q8G3/dPN9ejXn3aAJvB04ntcWfUrRcqbv1bINqvDWyrG8GzSBt1aOpXTdylkduk0ZUTZ9ZJcc0TlXSvVTSnmkmY5QSj1ng9cJVEoVND/eyez163Q6Jv8wDn//3lSr3pQe3QOoVKm8VZv/vfoyiQlJVKrcgB8m/8b48aMAqFSpPN27daC6dzP8/Hrx4+Tx6HQ6wsNP41PLF59avtR+qTU3btxk2fI1mR26Jf5vvh1Dl07/o7ZPKzp39ef5iuWs2rzStyuJiUnUqN6Mn6dM57MvRgBwLCycJg0DaFjPn84Br/L95LHo9XoAJkz6lPXBW6j1oi/16/gRfuJUpsb8ww9j8W/fh+rVm9K9ewcqVbTe5q++2oOExCQqV27A5Mm/MX7cRwBUqliebt064O3dDD//3kyePA6dTscLlZ/ntf+9TL36ftT08aVt2xaUK1cagF6936FW7VbUqt2Kv5cFsmzZ078XtthvAL779nPWrd1I1aqNqVmzJceOn7Ssz8vLgxbNG3HuXORTx39/LhO/GU33zm9Qv1ZbOnXxo8LzZa3a9HrFtA/V9m7J1CkzGP3ZMKv5Y7/8iJDgLZbpU6fO0rRBB5o26EDzRh25cfMmq1cGZ2rcD8vnk4nD6f/yQPwbdKddp1aUrVDaqk10VCwfDvic1UvXpVv+zylzGPHu6CyJ9UkEtG3J1G/HZncYVnQ6HWMmjuB/3d+nVf3O+HdqTbn7tnnXXgEkJSbTrHYHpk+dy4jRAwHo3qcTAG0bdadvl7f56PMhKGX6DzZk7RY6+qbvmNkshwkjeb3HANrU74Jfx1bpcujSK4DkxGRa1A5g+tS5DPt0AADd+nQEwK9xd/p1fYcPPx+MUorr12/QvmlPyyM6MoZ1qzdkRTIUGjaQS4NGEtvjVZx9m+FQumS6ZjfXbyK+T3/i+/TnxopAAJyqvoBTtSrE9XqduJ6v4VT5eXK9WN32MVuFb/+fYaVT+H3ej9n9JvFTy+FUbV/X0vm+6/Dy7UxpPZJf2n7Etl9X0fqTXgBcT7jK3Ne+ZkrrkSwdOpXO372dHSmIfylHdM6BfoDHoxo9DqXUA8fRa5rWVtO0RKAgkOmd89q1anD6dARnz54nJSWFBQuX4+/fyqqNv78vs2cvAmDJktU0a9rA/HwrFixczp07d4iIuMDp0xHUrmVdYW7WrAFnzpzj/PmozA4dgJo+1Tlz5hwRERdISUlh6eJVtGvXwqpN23YtmDd3KQDL/l5D4yZ1Abh58xYGgwGA3LlzoWmmgWD58+ejfv1azJq5EICUlBSSkq5mWsy1anlbbfOFC5fjn6ZqD/dt86WraWrZ5r4svG+b16rlTcWK5di164Alp61bdtKhQ+t0r92lsz8LFi5/6hxssd/kz5+PBg1e4s/pfwF3t3uyZX1ffz2GDz8aZ3mfMsuLPtU4e+Yc58z70N9LVtPmvn2oTbvmzP/rbwBWLAuioXkfMs1rwbmIC5w4nvEBXKMmdYk4e57IC9GZGveDVHvxBc6fjSTyXDQpKakE/r2OZq0bWbWJvhBDeNgpjMb0d9zduXUP16/dyJJYn4SPd1VcCuTP7jCsVH+xCufORnLhXBQpKams+nstLdo0sWrTok0Tls5fBcCaFSHUbVgLgHLPl2H71t0AXL6UQHLSVap6myqFofsOczHuUpbkUO3FFzgXccGSw+pl62ieLofGLF1gyiFoZQh1G9a+l8MWUw5X7svhrpJlilPkuULs2XHA5rk4Va5IamQUhugYSE3lZvAGnBvVe7yFNQ2VywkcHVCOjigHBwxXEmwb8H2ehc+wl3dZrpyLI+HCRQwpBg6v3ElFX+szobev3bT87ZQnF5i/2mOPnuNqfCIA8eGROORyRO/07FxuqKFs+sguT9Q5V0oNV0oNMP/9nVJqg/nv5kqpOUopX6XUDqXUfqXUIqVUPvP8T5VSe5RSR5RS05RJF8AHmKuUClVKOZtf5n3z8oeVUhXNy+dVSv1pXscBpVQH8/P9zK+zElinlHJXSm0xr++IUqqhud3divwEoKx5/ldPvvmseXi6ERl5r9MQFRWDp4dbujYXzG0MBgNJSckUKVIIT4/0y3p4Wi/bvVsHFixYllnhpo/fw5WoyJg0McTi7uFq1cbdw83SxmAwkJx0lcLmYRE1faqzc88atu8KZPDATzAYDJQqVZxLl67w89RJbP1nBT/+NJ48eZzJLJ4e7kResI7Zw9P9vjZuRKaJOSnZtM09PN0tzwNERcbi6eHO0bATNGz4EoULF8TZOTetWzfDy8v62LFBg5eIj7/IqVNnnzoHW+w3ZcqU5NKly/zx+3fs2b2WX6d+Zdnufn4tiY6K4dChsKeO/X7u7q5ER8ZapqOjM9iH3F2t96HkqxQuXIg8eZwZMPgNvprw0wPX37FzO5YuXp3pcT9IMbeixEbFWabjYuJxdS+aZa//X+TqXpSY6Hv7UGx0PK7uxazauLkXJSbK1MZgMHA1+RqFChfk+NFwWrRujF6vx6uEB1WqV8Ld03r/ywpu7sWISbPfxEbHpdtvXNPsWwaDgWt3czgSTos2TR6ag3/H1qxeljVnj/TFnsMQF2+ZNsRfQl80/WfAuWlDis35jcJfjkZfzDT/zpEwbu8LxWP1YtwDF3Fr5x5SI85nSdx3PQuf4fyuhUmKvmyZTo65QgHX9MMRa/dpyaDN3+I78mVWj5mZbn7lNrWJOXoOw51Um8Yrnt6TVs63AA3Nf/sA+ZRSjkAD4DDwMdBC07QXgb3AEHPbnzRNq6VpWhXAGfDTNG2xuU0vTdO8NU27e/h3ybz8L8AH5udGARs0TasFNAW+UkrlNc+rC/TVNK0Z0BNYq2maN1AdCL0v/pHAafPrDSOT3D19mtb9lcmM2zx6WUdHR/z8fFm8ZFUmRJqxB8Vm3Sb9cnfj3Lf3IHVqtaFp444MGfoWuXI54eDgQHXvF/jj97k0rN+e6zduMnho5o1vfVg899pkvG0ftOzx46f46uufWRP4F6tWzuHQ4TBSU62/zLp375ApVfOHxffoNg9e1kGvp0aNqvz66yxq1W7F9es3GD78PZydc/PhyAGM+ezrTIn9fk+cCxojPhrA1CkzuH494yqVo6Mjrds2Z8XfthnWlZHH+UyIzJXRNn+cLyJN01g0dzmxMfEsWz+Hj8d9wP7dBy1n9LLUE3+mNRbPW0FsdBx/r5/NqLFD2b/nIKmp1jm06+jLqqVBmRvzAz36/bi1dQcxAT2J7/0Gt3fvp9DokQDovTxwKFWCGP9uxPh1I5dPDZy8q2VF0BbPwmf4cf6fA9g9O5jvGw9h3YT5NH4/wGpe0fKe+I7swYqP/rBVmNnCaONHdnnSzvk+oKZSKj9wG9iBqZPeELgJVAb+UUqFAn2BuwPUmiqldimlDgPNgBce8hpL07xWKfPfvsBI83o3AbmBEuZ5wZqmXTH/vQd4VSk1Bqiqadq/GkehlOqvlNqrlNprNF5/7OWiImOsKqyenu5Ex8Sla1Pc3Eav1+PiUoArVxKIjEq/bEz0vWVbt27KgQOHiY+33WnZqKhYPL3uVZ09Pd2IvS/+6DRt9Ho9BVzyk3Al0apN+InTXL9xk8qVnycqKoaoqFj27T0IwPJla6he/WFv+78TGRWDV3HrmNNW3Sxt0sTsUqAAV64kmt+vNMt6uREdY1p2xoz5vFSnDc1bdCHhSqJVhVyv1xPQoQ2LFq3MlBxssd9ERsUQGRnD7j2m095Llq6mhndVypYtRalSJdi3N5iT4Tvx8nJn9661uLpmTiUpOjoWD697VX8PDzdiY+LTtbHahwqY9qEXfaoz+vNh7D+8gTff7sugD97itf69Lcu1aNmIQwePcvHiZbJKXEw8bmmqlq7uxYiPvZhlr/9fFBsdj3uaM0duHsWIu2+bx0bH424+s6jX68lfIB+JCUkYDAbGffwN/k1f5q0+Qyjgkp+I01lbqTXFF2dV7XbzcCU+1vq7OzbNvqXX68mXJofxn3xL+6Y9efuVoRQokJ9zZ+7lUPGF8ugd9Bw9dDxLcjHEX0Tveu/Mhb7YcxguWediTE6GlBQAri9fjZP5uh/nJg25cyQM7eYttJu3uLVjN05VKmVJ3Hc9C5/h5NgruHgUsUwXcC9sGaqSkSMrd1Cppc+99m6FefnXwSwdMpWE8/EPXE7kHE/UOdc0LQWIAF4FtgNbMVWyywJnMXWUvc2PypqmvaaUyg38DHTRNK0q8BumzvWD3Db/a+De/dgV0DnNuktomnbMPM/Si9Y0bQvQCIgCZiul/tVVQJqmTdM0zUfTNB+dLu+jFzDbszeUcuVKU6pUcRwdHenerQOrVllfYLJq1Tr69OkKQOfO7di46R/L8927dcDJyYlSpYpTrlxpS8cKoHv3AJsOaQHYv+8QZcuWomRJLxwdHenUxY/AwBCrNoGBIfTsZbroKqBjG7Zs3gFAyZJelgtAixf3oHz50pw7H0l8/CWiomIoV950AU7jJvUeOJ74Sezde9Bqm3fr1oFVq6xP965aFXxvm3dqxybLNg+m233bfM8e00mWokWLWHIJCGjDggX3quTNmzfkxInTREXFkBlssd/ExV0kMjKaChVMF2M2a9aAY8fCOXLkOJ5e1SlfoQ7lK9QhMjKG2i+1Ii4uc/6zOrDvMGXKlKKEeR/q2LkdQfftQ0GBG+jxsumit/YBrdlq3of8W/fkxarNeLFqM379ZSbffz2VP6bNsSzXqasfSxfZ7sxRRg4fCKNkmeJ4lvDA0dGBth192bh2a5bG8F9z6MBRSpUpjpd5m/t1bEVI0GarNiFBm+nUww+ANu2bs2PrHgByO+fGOY/pv5X6jV8i1WDgVPjTDz37tw4fCKNU6Xs5tAvwzTiH7qYcWvs3Z+e2jHMw3JeDX6fWrFqaOXeJehx3jh3Hobgnenc3cHDAuWUzbm7ZYdVGV6Sw5e/cDeuRYh66YoiNI1eN6qDXgV5PrhrVs3xYy7PwGY46eIbCpdwo6FUUvaOeqv51OB68z6pN4VL3DkAqNPPmcoSp0JS7QB56T/+A9ZMWcH5feJbGnRWe1THnT3NVwBZMw03+h2koy7eYqtw7gSlKqXKapp1SSuUBvIC7h2uXzGPQuwCLzc9dBR7nqqS1mMaiv69pmqaUqqFpWrorYpRSJYEoTdN+Mw97eRGYlabJ477ev2IwGBg46GNWr56HXqdjxswFhIWFM3r0B+zbd5BVq4L5c/p8ZsyYzLGwbSQkJNKrt+m61LCwcBYtXsmhgxtJNRgYMHCU5eIUZ+fctGjeiHfeGZHZIaeL/4Ohn7F02Qz0eh1zZi/m+LGTfPTxIA7sP8yawBBmz1zItN+/4cDBDSQkJPK/fqa7JNSp68PgoW+SkpKKZjQydPBorlw2XfgzfOhn/P7Hdzg6ORJx9gLvvj08U2MeNOgTVq+ai06vY+aMBYQdC2f0px+wb79pm0+fPp8Z038gLGwbCVcS6d3HvM2PhbN48UoOHtyAIdXAwIEfW7b5gvnTKFKkECkpqQwYOIrExCTLa3br2p4FCzPvQMlW+82gwZ8wa+aPODk5cubseV5/fcjDwsi0XEYO+5xFf/+BTq9n3uzFnDh+ipGjBhC6/whBazYwd9Yifp72FbtDg0lMSOKNVwc/cr3Ozrlp3LQeQwZ+YvMc0jIYDIwd+RW/L5iMTq9j6byVnDpxhvdH9OdI6DE2rt1KFe9K/DhjEgVcCtDUtyHvD++Pf6MeAMxeMY0y5UqSJ68zG0NX8vHgcfyzcWeW5vAww0ZPYM+BQyQmJtM8oDfvvNaHzvddjJzVDAYDn42cyIxFU9DpdCyet4KTJ84waORbHA4NIyRoCwvnLuObn79gw+7lJCYmMfCNDwEo8lwhZiyagtGoERcTz9C37+0vI0YPxL9za5zz5GbboTUsnLOMyZN+tV0OH07iz4U/odfpWfzXck6dOMPAEaYcNqzdwqK5y/n65y9Yv3sZiQlJDO7/kSWHPxf+hGbUiI2J54N3rPf5tu1b8PrLA20Sd8bJGEn8+keemzwRpdNzfeUaUs9GUKB/P+4cC+fW1u3k694J54b10AwGjMnJJHw+EYCbG7aQy6cGrnP/ADRu7djDrW07Hv56mR3+M/AZNhqMrP50Bq/MGoFOr2P/ws1cPBlFs8GdiTp8lhPr9/NSX1/K1q+CIdXAraTrLB06FYCXXvGlcElXGg/oSOMBpqLIrD4TuH45+WEvKbKZetK7NSilmgNBQEFN064rpcKBqZqmfauUagZMBHKZm3+sadoKpdRYoAemqvsF4JymaWOUUp2B8ZiGxNQFjgE+mqZdUkr5AF9rmtbEfLHo90A9TFX0CE3T/JRS/czt3zPH1hcYBqQA14BXNE07q5SKSLPeeUA1YM3Dxp07Onna2eg0a3mcHnZyIue7mXL70Y1yuMy+I0pWc8n9+GePcqqiuQtmdwhP5VDY/OwO4alVrNglu0N4KrqMBv7akY1lXLI7hKfS8uy17A7hqXXNU/7RjXK4zyPm5qgPQpBrD5v+B9s6bn625PvElXNN00IAxzTTFdL8vQGolcEyH2O6WPT+55cAS9I8VSrNvL1AE/PfN4F0v4yjadoMYEaa6ZlAukuVNU1Lu96eGeUlhBBCCCFEdnl2bnYphBBCCCH+M7Lzjiq2JJ1zIYQQQghhd7Lzok1byim/ECqEEEIIIcR/nlTOhRBCCCGE3TE+m4VzqZwLIYQQQgiRU0jlXAghhBBC2B2jjDkXQgghhBBC2JJUzoUQQgghhN2x75/4ezCpnAshhBBCCJFDSOVcCCGEEELYnWf1R4ikci6EEEIIIUQOIZVzIYQQQghhd4xK7tYihBBCCCGEsCGpnAshhBBCCLvzrN6tRTrnj/CeR8PsDuGplDfY91t8Up+a3SE8tV9it2d3CE/FYLT/S27ibiZkdwj/ecePL87uEP7TGlb7X3aH8FT0yv5P9I8cnD+7QxB2wr57bkII8R9QsWKX7A7hqUjHXAhhC/ZfOsqY/R+KCiGEEEII8YyQyrkQQgghhLA7xmfzZi1SORdCCCGEECKnkMq5EEIIIYSwO0aezdK5VM6FEEIIIYR4Akqp1kqpE0qpU0qpkQ9p10UppSmlfB61TumcCyGEEEIIu6PZ+PEoSik9MAVoA1QGXlZKVc6gXX5gALDrcfKSzrkQQgghhLA7RmXbx2OoDZzSNO2Mpml3gPlAhwzafQFMAm49zkqlcy6EEEIIIcR9lFL9lVJ70zz639fEE7iQZjrS/FzaddQAimuatupxX1cuCBVCCCGEEHbH1j9CpGnaNGDaQ5pkVF+3jIhRSumA74B+/+Z1pXIuhBBCCCHEvxcJFE8z7QVEp5nOD1QBNimlIoA6wIpHXRQqlXMhhBBCCGF3HueiTRvbA5RXSpUGooAeQM+7MzVNSwKeuzutlNoEfKBp2t6HrVP/LIYAACAASURBVFQq50IIIYQQQvxLmqalAu8Ba4FjwEJN044qpT5XSrV/0vVK5VwIIYQQQtidx7yjik1pmhYIBN733KcPaNvkcdYplXMhhBBCCCFyCKmcCyGEEEIIu2Pru7VkF6mc21DFxtX5MORbPtr0Pc3fTj/0qPFrbRkR/DXD1kzk7bkfU8jTcs0AfiN7MnztVwxf+xXefnWzMmyL4k2q8fKmr+i19RtqvOOfbv4LvZvRPfhLugWNo+OSTyhU3gOA8gH16BY0zvJ4+9wsilQukdXhA/b5HrRs2ZhDhzZy9OgWPvjgnXTznZycmD17CkePbmHLluWULOkFQOHCBVm7dj6XLh3ju+8+t1qmSxd/9uxZy/796xk37iObxt+8RSN271/HvoMhDBryZobx/zHzB/YdDCF442KKlzDdEvbFmtXYsn0FW7avYOuOlbTzb2lZ5s13+rJ9dyDb96zhrXf62TR+Uw4N2bV/LXtD1zNwyP23tTXnMON79oauJ3jDvRzu8vRy53xMKO8NeA2AcuVLs/mfFZbHuagDNs2jUbN6BO9cyobdy3lzQPrXcXJyZPLvE9iwezlL1s7Es7g7AI6ODkycPIbALQtYtWk+L9WvaVlm6Efvsu1gIIcittks7ifx8fhvadSuBwG938ruUJ6IPcRfp0ltFmydxaJ/5tLnvZ7p5nu/VI2Za6ex7XwITds1tpr3z4UQZgX/zqzg3/lqxrisCtlK/aZ1WPnPAgJ3LuK19/ukm1+zjjcLg2cSGrWNln5NreZN/es7tocHM2XO11kVboZ0JSuT+5Ux5O77OQ4+rdLNd2zUldw9R5ker3yG81vfmpbzqnDv+Z6jcH73R/Rlqmd1+OJfyvGdc6VUQaVU+h5KDqd0is6f/49p/SYwseVQarSvj2s56//Ao8Ii+Nb/I75qM4KDa3bh/2EvACo3rYHXC6X4uu0Ivg/4mGb9/ciVzznL4280ti+rX5nEX82GU75DHUvn+67wZTtY0PJDFrYexYGpq6n/aW8ATi7bzsLWo1jYehTrB/1C8oVLXA47n6Xx383B3t4DnU7HDz+MpUOHvnh7N6dbt/ZUrFjeqk2/ft1JTEzihRca8eOPvzN27IcA3Lp1m88++4aRI63/AyxcuCBffvkRbdq8zIsvtsDV9TmaNq1vs/i/+nYMXTu9Rh2f1nTu6sfzFctZtenTtytJiUnUrN6cX6ZMZ8wXwwE4FhZO04YdaVSvPV0C/sd3k8ei1+upVLk8fft1p3njTjSs40erNk0pU7akTeK/m8Okb8bQrdPr1K3Vhs5d/Hj+eescer/ShcTEZHy8W5hy+HyY1fzxE0YRErzFMn3q5Fka129P4/rtadowgBs3b7Jq5TqbxT9m4gj+1/19WtXvjH+n1pSrUNqqTddeASQlJtOsdgemT53LiNEDAejepxMAbRt1p2+Xt/no8yEoZRrUGbJ2Cx19X7FJzE8joG1Lpn47NrvDeGI5PX6dTscH4wcyuNcIXm7SF98OzShV3vrzFxcVzxeDJrDu7/Xplr996w6vtHydV1q+zrB+o7IqbAudTsfHEz7g7Z6Dad/wZdp29KVMhVJWbWKi4vh44BcELk3/mZz+81w+fO+zLIr2AZTCqcnL3F72E7dmf4ZDhVqowu5WTVK2LOLWvHHcmjeO1IMbMZw6AIAxMtzy/K0l30HqHQznw7IjC5sw2viRXXJ85xwoCNhd57yEdzkunYvl8oV4DCkGDqzcThVf69tantoRRsqtOwCcO3CSgm6FAXAt78npXccwGozcuXmbqGPnqdQ4a490i3mXJSkijuTzFzGmGDi1YielfWtatUm5dtPyt0OeXGha+psale9Qj1Mrdtg83ozY43tQq5Y3p09HcPbseVJSUli0aCX+/r5Wbfz9fZkzZzEAS5cGWjraN27cZPv2Pdy+bf3rwKVLl+DkybNcunQFgA0bthEQ0MYm8df0qc6ZM+c4F3GBlJQUli5eTdt2LazatGnXgr/m/g3A8r+DaNzEdFbi5s1bGAwGAHLlvrc/VXi+HHt2h1rm/7NtN373bZPMzaEaZ9PmsGQ1bfyaW7Vp264F8+ctNeWwLIhGTe6dWWnr14KIiAscP3Yyw/U3blKPiLPnibwQneH8p1X9xSqcOxvJhXNRpKSksurvtbRo08SqTYs2TVg63/RjdWtWhFC3YS0Ayj1fhu1bdwNw+VICyUlXqepdGYDQfYe5GHfJJjE/DR/vqrgUyJ/dYTyxnB5/5RoViYyIIvp8DKkpqQQv30CjVtYH9zGRsZw6dgbNmANubHefqi9W5vzZSCLPRZOaksqaZcE0a93Iqk30hRjCw05hzCD+XVv3cuPajawKN0M611JoSfFoyZfAaCA1fA/6MtUe2F5foRap4env1Kcv/yKGiKOQmmLLcEUmsIfO+QSgrFIqVCn1lVJqmFJqj1LqkFLqMwClVCml1HGl1O9KqSNKqblKqRZKqX+UUieVUrXN7cYopWYrpTaYn3/DVkEXdC1MYvRly3RSzBVcXAs/sP1L3ZpybFMoANHHzlOpiTeOuZ3IWyg/5etWpqB7EVuFmqG8boW4Fn3FMn0t5gp53Qqla1elbwt6bfuGeh/1YNuns9LNL+f/EieXZ0/n3B7fAw8PNyIj73XaoqJi8PBwfWAbg8FAcvJVihRJ/97cdfr0OSpUKEvJkl7o9Xr8/X3x8vJ4YPun4e7hSlRkjGU6OioW93Tx32tjMBhITrpGYXP8NX2qs33PGv7ZtZohAz/BYDBwLCycevVrUahwQZydc9PStwmeXtZVo0zNwd2NqKj7cnC3zsGUZ2y6HPLkcWbg4P5M+vLHB66/U5d2LFn02L/i/K+5uhclJjrWMh0bHY+rezGrNm7uRYmJuhf/1eRrFCpckONHw2nRujF6vR6vEh5UqV4Jd0/r3MV/S1G3osRHX7RMx8dcpKh70cde3imXE9PX/MrvK3+mUesGtgjxoYq5FSU2Ot4yHRcdTzG3x48/J1D5CqFdTbBMa9cSUfky/s5X+Qujc3kO44Xj6eY5VPAhNXyPzeLMDpqy7SO72MMFoSOBKpqmeSulfIEuQG1MP5m6QinVCDgPlAO6Av0x3RS+J9AAaA98BASY11cN0y805QUOKKVWa5pmVcJSSvU3r4fmhX2omr/sv486wx90zbiqUDOgAcWrleGn7qZTZye2HqJ4tTIMXPo51y4nE7H/JEZD1p5guXsqO62Mwj8ycz1HZq6nfEBdag4IYMOQXy3zinmXJfXmHa6ciLRlqA9mh+9Bxttd+9dt0kpMTGLAgFHMnj0Fo9HIzp37KF3aNtcAPFZsD2mzb+9B6tVqQ4Xny/Lzr5NYv24z4SdO88N30/h7xUyuX7/O0SPHSE012CT+B4T32O/ByFED+OWn6Vy/nnGlzdHRkdZtm/H5aNuNX80otnT7/QPiXzR3OWUrlGbZ+jlERcawf/dBy9kM8d+U0e70oO/RjATU6saluMt4lHBnyqLvOH3sDFHnbHPWKCMZflaz7NVt6AHvgb6CD6kn96efn6cAuiKeGM8dzYLgxNOyh855Wr7mxwHzdD6gPKbO+VlN0w4DKKWOAiGapmlKqcNAqTTrWK5p2k3gplJqI6aO/rK0L6Jp2jRgGsDgUj2e6HOcGHuFgh73Kq0u7oVJik9I165C/Sq0fK8jP3X/DMOdVMvz66csY/0UU1i9f3ifi2dj0i1rS9dirpDP416VOZ97YW7EpY//rpPLd9Jo3KtWz5XvUCfbquZgn+9BVFSMVVXb09OdmJj4DNtERcWi1+spUCA/V64kPnS9gYHrCQw0jQd97bWeGGx0oBEdFWtV1fbwdCP2vvjvtomONsfvko+E++IPP3GaGzduUqlyBUIPHGHOrEXMmbUIgE9GDyU6TWU403OIjsXT874cYjPKwS1dDjV9qtO+Q2vGfDEcF5cCGI1Gbt26ze/T5gDQwrcRh0LDuHjxMrYSGx2Pu4ebZdrNoxhxsRfTtzG/N3q9nvwF8pGYkATAuI+/sbRbFDidiNNZf72IyDniYy5SzONepbmYe1Euxj7+8KZLcaZ9Pfp8DPu3h1KhSvks7ZzHxcTj5nHvzJGrRzEu3vd5yOm0awmo/Pcq5SpfQbTrGX/nO1Tw4c6m+Rk+bzgdCsZn6/4mz1Y299jDsJa0FPClpmne5kc5TdP+MM+7naadMc20EeuDkPs72zY5iL5w8DRFS7lR2Ksoekc9NfzrcTR4n1UbzxdK0XX8G/z++ldcu5xseV7pFHkK5gPAvWIJPCqW4MTWQ7YI84HiD57BpZQb+YsXReeop1z7OpwN3m/VxqXUvdPdJZt7kxSRpsOkFGXbvZRt483BPt+DvXsPUq5caUqVKo6joyNdu/qzalWwVZtVq4Lp3bsLAJ06tWXTpu2PXG/RoqaDlIIFXejfvw/Tp/+V+cED+/cdomzZkpQo6YWjoyOdurRjTWCIVZugwBBe7tURgA4dW7Nl804ASpiH3QAUL+5BufKlOX8+CoDnipoOFL283PHr4MviRSttEr8ph8OUKVvqXg6d2xG02jqHNYEh9OhpuniyQ0BrtppzaNeqJ95VmuJdpSlTf57Bd99MtXTMATp38WPJYtsNaQE4dOAopcoUx6uEB46ODvh1bEVI0GarNiFBm+nUww+ANu2bs2Or6VR3bufcOOfJDUD9xi+RajBwKvysTeMVOdux0BMUL+2Fe3E3HBwdaNmhGVvXPfo7ByC/Sz4cnRwBcCnsQrVaVTgbHmHDaNM7cuAYJcoUx7OEOw6ODrQJaMnGtVuzNIanZYw7hypYDFWgCOj0OFSoheFM+v+PVEFXyJ0XY8yZdPP0z+CQlmeZPVTOrwJ3r5ZZC3yhlJqrado1pZQn8G+vbOiglPoS07CWJpiGzWQ6o8HIkk+n8+asj9DpdexauJHYk5G0HtyVC4fPcHT9Ptp/2ItceXLR7+dBACREXeKPN75G7+jA+4vGAHDr2k3mDP4py4e1aAYjWz+Zif+c4Si9juMLNpMQHkWtoZ25eOgsEcH7qdrPF68GL2BMNXA76Tohg+8NafF4qSLXYq6QfD77KhT2+B4YDAYGDfqElStno9frmTlzAceOhfPpp0PYt+8wq1cHM2PGAv7883uOHt3ClSuJvPLKe5blT5z4h/z58+Pk5Ii/fyv8/Hpz/PhJvvlmDFWrmi7sGz/+e06dsk2Hy2AwMHzoZyxZNh29Xs/c2Ys4fuwkH348kND9R1gTGMLsmQuZ+vs37DsYQkJCIq/1M237unV9GDj0TVJTUjAaNT4YPJorl01nOmbNnUKhwoVITUlh2JAxJCUmPyyMp8/hg89YvOxP9Do9c2cv5vjxU3w4aiAHDhwmKHADc2YtYupvX7M3dD0JCYm8/urgR67X2Tk3TZrVZ/DAT2wW+934Pxs5kRmLpqDT6Vg8bwUnT5xh0Mi3OBwaRkjQFhbOXcY3P3/Bht3LSUxMYuAbpjv+FHmuEDMWTcFo1IiLiWfo2/diHTF6IP6dW+OcJzfbDq1h4ZxlTJ7064PCyDLDRk9gz4FDJCYm0zygN++81ofO/ulvNZdT5fT4DQYDX4/6gR/mfYVOr2PV/DWcDY/gjWGvcvzgCbau206l6s8z8Y+x5C+YjwYt6/LGB/3o2fRVSpUvyYiJQ9GMRpROx6wp84g4eS7L4x//4df8Ov8H9Hodf/+1itMnzvLu8Dc4evA4m9ZupYp3Jb6fPpECBfPTxLcB7w57g4DGpltGzlw+ldLlSpInrzPrD6zg08Hj2L5pV5bmgGbkzqYF5AoYAEpHath2tCsxONbxxxh3DsNZU0fd4flaGDLogKv8RVD5C2OMzPgidXv2rFbO1cPGquYUSql5mMaKrwEigdfNs64BvQEDsErTtCrm9jPM04uVUqXuzlNKjQE8gLJACWCSpmm/Pey1n3RYS05R3mAPx18PdlKf+uhGOdwvsY9XZcqpnB2csjuEp5bhOGw7UihXzr2bx+M4fnxxdofwn9ew2v+yO4Sncs1w69GNcrjdH3lndwhPLc/AqTnqy/Sn4r1t2kd778KcbMnXLnpumqbd/6sHP2TQrEqa9v3S/B2Rdh4Qrmla+l8VEUIIIYQQdsOuq6cPYW9jzoUQQgghhHhm2UXlPLNomjYmu2MQQgghhBBPz5ijBtlknv9U51wIIYQQQjwbntULQmVYixBCCCGEEDmEVM6FEEIIIYTdkcq5EEIIIYQQwqakci6EEEIIIeyO3EpRCCGEEEIIYVNSORdCCCGEEHbnWb2VolTOhRBCCCGEyCGkci6EEEIIIeyO3K1FCCGEEEIIYVNSORdCCCGEEHZH7tYihBBCCCGEsCmpnD+CE/Z9KbCjnR9W2vv2B9Dr5BhYPB2dsv/PgcheWw/9SZPqr2d3GE8st84xu0N4evJ/QaYzPqO1c9lThBBCCCGEyCGkci6EEEIIIeyO3K1FCCGEEEIIYVNSORdCCCGEEHbn2RxxLpVzIYQQQgghcgypnAshhBBCCLsjY86FEEIIIYQQNiWVcyGEEEIIYXeMz+hPQEjnXAghhBBC2B35ESIhhBBCCCGETUnlXAghhBBC2J1ns24ulXMhhBBCCCFyDKmcCyGEEEIIuyO3UhRCCCGEEELYlFTOhRBCCCGE3ZG7tQghhBBCCCFsSjrnWaRC4+p8EPINwzZ9R5O326eb/1KvFgwKmsjAwC95a9FoipXzzIYorXk1qUbXzV/Rbds3VH/XP938Sr2b0Xn9l3RaOw7/pZ9QsLwHAMpBT+Pv3qTz+i/psnFihstmB3t5D1q2bMyB0BAOHd7E0KFvp5vv5OTEzFk/cejwJjZtXkaJEl4ANGvWgG3/rGT37iC2/bOSxo3rWpZxdHTkx5/GE3pwA/sPhNChQ2ubxd+8RSN271/HvoMhDBryZobx/zHzB/YdDCF442KKl7Dezl5e7lyIPch7A14DIFcuJ9ZvWsLWHSvZvmcNI0cNtFns93JoyK79a9kbup6BQ/pnnMOM79kbup7gDelz8PRy53xMqCUHgAIu+Zkx+0d27gti594gatX2tln8DZvVZe2OJazfvYz+A/plEL8j3//2Jet3L2Nx0Ew8i7sD4OjowITJo1m1eQErNv5F7Xo1AcibNw8rNs6zPHYdD2HU2KE2i//f+Hj8tzRq14OA3m9ldyhPxB7if6lJLf7aMpMF22bT+92X082v/lI1/gz6lc3ngmnSrpHVPFePYnw3bxJzN01nzsY/cfNyzaqwrdRtWpslW+fy9/a/6Pter3Tza9Spzpx1f7Dzwkaat2uSbn7efHkI3L+U4eMGZUG06f1z7jIBc3bQfvZ2/twXkW7+11vD6T5/F93n76LD7O00nLbZMu/7f07Sed5OOs3dwcQtJ9C0Z6farNn4kV1y/LAWpdRHmqaNz+44nobSKQI+f5Xfe48nKfYy760YR1jwPuJPRVnahC7/h11z1wNQqUVN/D7pw599J2RXyCidov7YvgT2nMD1mCsErP6cc+v2kXgy2tLm1LIdHJuzAYASLV+kzujeBPWeRBm/2uidHFjS4kP0uZ3ounEip5fv4FrkpexKx27eA51Ox7fffY6/X2+iomLZunUFq1cHc/z4KUubvv26kZiYRLWqTejSxZ8vxo6k7yvvcflyAl26vEZsTDyVK1dg+YpZlC9XB4DhI97j4sXLeFdvhlKKwoUL2iz+r74dQ8f2fYmOimXDlqWsCQzhRJr4+/TtSlJiEjWrN6dTl3aM+WI4r/W91+EeN3EU64O3WKZv375Dh3Z9uH79Bg4ODqwJns/6dZvZuyfUZjlM+mYMnTr0IzoqlpDNSwhavYETJ+7l0PuVLiQmJuPj3YJOndsx5vNhvNbv3n/a4yeMIiRNDgBfTvqYkPVb6NfnfRwdHXHOk9tm8Y+ZMJJ+Xd8hNjqOJetmsyFoM6fCz1radOkVQHJiMi1qB9AuwJdhnw5g0Bsf0q1PRwD8Gnen8HOF+GP+j3Rqadr27Zv2tCz/9/o5rFu9wSbx/1sBbVvSs3N7Pvri6+wO5Ynk9Ph1Oh1Dxw1k0MvDiI+5yO+Bv7Bt3XYiTp6ztImLimPc4Im8/Fa3dMt//MNIZk2ey56t+3DOkxujMeu7PDqdjhHjh/Bu98HExVxk1prf2LLuH86GR1jaxEbGMWbgePq83SPDdbw14nX277DNd86jGIwaEzaf4JcONXDNl4teC/fQuPRzlC2cz9Lmg4YVLH//dfACJy5dBSA0JpHQmCQW9ngJgFeX7GVfVCI+XoWyNgnxr9hD5fyj7A7gaRX3Lsflc7FcuRCPIcXAwZU7qOzrY9Xm9rWblr+d8uSCbD6yLepdluSIOK6ev4gxxcDp5Tsp6VvTqk1Kmpgd08asgUOeXCi9DofcThhTUq3aZgd7eQ98fLw5c/ocEREXSElJYfHilfj5+Vq18Wvny9w5SwD4++9AmjSpB8DBg0eJjYkHICwsnFy5cuHk5ATAK6905euvfgZA0zQuX06wSfw1fapz5sw5zpnjX7p4NW3btbBq06ZdC/6a+zcAy/8OonGTexX+tn4tOHf2AsePnbRa5vr1G4Cpsuvo6GjTyk9Nn2qcTZvDktW08Wtu1aZtuxbMn7fUlMOyIBrdl0NEhHUO+fPno169WsyeuQiAlJQUkpOu2iT+ai++wLmIC1w4F0VKSiqrl62jeZsmVm1atGnM0gWrAAhaGULdhrUBKPd8GbZv2Q3AlUsJJCddpap3ZatlS5YpTpHnCrFnxwGbxP9v+XhXxaVA/uwO44nl9Pgr1ahIZEQU0edjSE1JJWT5Bhq2qmfVJjYyjtPHzqAZre+dUap8SfQOevZs3QfAzRu3uH3rdpbFftcLNSpxISKKKHMO65aH0LhVA6s2MZGxnDp2OsODh4rVKlDkucLs3Lwnq0K2ciQumeIuzni5OOOo19GqvCubzjy42BV0Mo7W5U1nKBSKOwYjKUYjdwxGUo0ahfM4ZVXoNme08SO75KjOuVJqmVJqn1LqqFKqv1JqAuCslApVSs01t+mtlNptfu5XpZTe/Pw1pdRE8/LrlVK1lVKblFJnlFLtzW36KaWWK6WClFInlFKjsyIvF9dCJEZftkwnxVzGxTX9UWvdPi0Zvvl72o7syfIxM7MitAfK616IazFXLNPXY6+Q1z19zJX7tqD7tm+oPaoH2z+dBcCZ1btJvXGbXvt/4uXd33Po10BuJ17PstgzYi/vgYeHK5FR985OREXF4O7h+sA2BoOB5OSrFClinUtAQBsOHTzKnTt3cHEpAMCnnw7ln+2rmD1nCsWKPWeT+N09XImKjLFMR0fFZhj/3TYGg4HkpGsULlKIPHmcGTj4TSZ++WO69ep0OrZsX0H42V1s2rCNfXsP2iR+AHd3N6Ki7svB3ToHU56xD8ihP5Puy6FkqeJcunSFn6ZOZNO25fzw0zjy5HG2Sfxu7sWIiYqzTMdGx+HqXtSqjatbUWLNbQwGA9eSr1GocEGOHwmnRZsm6PV6vEp4UKV6Jdw9rXP379ia1cuCbRK7yHmKuj1HfHS8ZTo+5hJF3Yo+ZIl7ipfx4lryNcb/9hnT1/7Kux+/iU6X9d2OYm5FiYtKm8NFirk93negUorBo9/jhy9+tlV4jxR//Rau+e+daXPNl4uL1zM+yIlOvkl08k1qeRUGoLq7Cz6ehWj55zZ8p2+lXokilCmcN0viFk8uR3XOgf9pmlYT8AEGAF8BNzVN89Y0rZdSqhLQHaivaZo3YADuDh7LC2wyL38VGAu0BDoCn6d5jdrmZbyBrkop6/IpYD4w2KuU2ht69dT9s/89pdI9lVHhb8fsYCY1HsSaCfNo/n7Hp3/dp5I+5owGYIXNXM+CBkPZPX4+NQYEAFDMuwya0cjcmu8zv+4QqvZvS/4Sj/dlbjN28h6oDOPU7m/00DaVKpXni7Ejef9900knBwc9Xl4e7Nixl/r1/Ni9az/jx9vmhNTTxD9y1EB+mTLdUiVPy2g00qhee154vgEv+lSnUuXymRbz/TIIL10OD8pz5KgB/PJT+hwcHPRU936B6b/Po0mDDty4fjPD8fiZ4jHegwfFv3jeCmKj4/h7/WxGjR3K/j0HSU01WLVr19GXVUuDMjdmkWM91mf6AfQOeqrXrspPX0zl9bZv41HCnbbdWmV2iI+W4Wf68Rbt2q8j/4TsJC7NAUpOtvZkHM3LFkOvMyV9PvEGZxOus7Zffdb2a8DuyCvsi7LNmdPsYESz6SO75LQx5wOUUnd7RMWB+/8Hbg7UBPaYvzCcgbufmDvA3f8xDgO3NU1LUUodBkqlWUewpmmXAZRSS4EGwN60L6Jp2jRgGsCIUi8/9buTFHuFgh5FLNMu7kVIjn/wh+Pgyh10HPvaA+dnhesxV8jnXtgyndetMNdjHxzz6eU7aTD+VTYDZQPqcWHTIbRUA7cuJxO3J5yi1cpw9fzFLIg8Y/byHkRFxeLl6WGZ9vR0twxVuSva3CY6Kha9Xk+BAvm5ciURAA9PN/6a/ytvvD6Es2fPA3D5cgLXr99gxYq1ACxdGsgrfbvbJP7oqFg8vdwt0x6ebhnG7+nlTnS0OX6XfCRcScSnVnU6BLTmsy+G4+JSAKPRyO3bd/jt19mWZZOTrrJt6y6at2jEsTDroS+ZlkN0LJ6e9+UQm1EObulyqOlTnfYdWjMmTQ63bt1mxbIgoqNiLRX/5cuDbNY5j42Os6p2u3m4Eh9rfQo8NiYeN09XYmPi0ev15CuQj8SEJADGf/Ktpd2C1X9y7sx5y3TFF8qjd9Bz9NBxm8Qucp74mIsU8yhmmS7m/hyX4h7v+qGLMRcJP3KK6POmM1Fb1v7DCy9WgvlrbBLrg8THXMTVM20ORbn4mDlU9XmBGi9Vp0u/APLkdcbB0ZEb12/y0/hfbRVuOsXy5ibu6i3LdNy12xTNmyvDtmtPxjGy8fOW6Y1nLlLVzYU8TqbuXv2SkPEG7AAAIABJREFURTgcl0xNTxlznpPlmMq5UqoJ0AKoq2ladeAAcP8VUwqYaa6ke2ua9rymaWPM81K0e4fzRuA2gKZpRqwPQu7vbNv80Cjy4GmKlHKjkFdR9I56qvvX5VjwPqs2RUq5Wf6u2KwGlyJibR3WQ108eIYCpd3IX7woOkc9ZTvU4Xzwfqs2BUrf6wCUaO5N0llTzNejL+NR7wUAHJxzUezFciSejiY72ct7sG/fQcqWK0XJkl44OjrSpYs/q1dbDyFYHRhMr96dAejYsS2bN28HwMWlAEuXTGf0p5PYudM6t8DAEBo1Ml0c2rRpfY4ft03Hdv++Q5QtW5IS5vg7dWnHmsAQqzZBgSG83Mt0DN6hY2u2bN4JQFvfl6n+QhOqv9CEX36ewbdf/8Jvv86myHOFKeBiGpObO3cumjStx8nwMzaJ35TDYcqULXUvh87tCFptncOawBB69OxkyiGgNVvNObRr1RPvKk3xrtKUqT/P4LtvpvL7tDnEx18iKiqGcuVLA9C4cV2ri2Qz0+EDYZQqXRyvEh44OjrQLsCXkKDNVm1CgjbTqbsfAK39m7Nzm2ksbW7n3JYLVes3fgmDwWB1Ialfp9asWrrWJnGLnOl46HG8SnviXtwNB0cHmndoxrZ1Ox5r2WOhJ8hfMD8FC7sAULN+DSLCzz1iqcwXFnqc4qW98CjujoOjA74dmrNl7bbHWvaTd7/Az6cL7Wt34/vPfiZwUVCWdswBXnDNz/mkG0Ql3yTFYGTtyTialE4/LCci4TrJt1Op7uZiec4tf272RSWQajSSYjCyPzqR0oXyZGX4NiV3a7E9FyBB0/7P3n3H13z9cRx/nXuTCJUgRpa9WtQeVXvGSoxSq1ZbWkqpvfcorVpFrZq1KYrYO/ZIYgSxgmwkQREyvr8/ElduboziZvh9no+Hh3y/53y/eZ+cfO8999xzv9EeK6U+ASrG749SSllqmhYF7AE2KaWmapoWqpSyA2w0TfsvV3vd+OOeAE2Bb95nI5ISGxPLphGL+XbpYHR6HSfX7Cfkij91e7fA/9wNLu4+TaWOLhSqXJyY6Gie3H/Emr5/mDvWK2kxsRwZvoQGywegdDourz5AuG8AZfs15473DW7tOkOxTi44VylGbHQMT+8/4kDvuAesC4t3UX3Kd7TYMxGUwnfNQcIu3k7R9qSVPoiJiaFvnxFs+mcper2epUvXcPHiFYYN782ZM+dw37qbJYvXsODPKZw9t5/w8Ag6dvgRgO+7diB/gTwMGtyTQYN7AtDYrT137txj+LCJLPhzCr/8MoK7d8P4/vv+Zss/oO9o1m9chF6vZ/mytVy6eIXBw3rhdeY829z3sGzJGuYs+I3T3nsID48wustJUhzsszN73q/o9Tp0Oh0b/nZnx/Z9ZslvaEO/0azbuBC9Ts/yZeu4dOkqg4f2wtPzHNvd9/LX0rXMmT+ZU167CQ+PoPPXvV973oH9xjJ3wW9YWVni53ebHt0GmS3/6MG/sHDNTPQ6PetWbuLq5ev0GtiVc14+7N1xkLXLNzF59lh2n9hIRPh9en8Xt8wpa7YsLFwzEy1WIzgolH4/DDc6d8PGdejcxvy3svwv+o+cyEnPs0REPKB203b88G17mrulwNKJt5Ta88fExDJ12O9MWTEJvU7PltXbuOHrR+d+nbjk7YvHriN8UvJjfv5zDDaZMlK57ud07tuJdrW+ITY2lllj5jB99WSUUlw+58s/K7amQBti+HXIVH5f+Rt6vY5/Vm3luq8f3/f/lovelzi48zBFS37CrwvHY5vZhqp1K/Fd/29oVaNDsmdNioVOx8BqH/PDJk9iNWhS1JECWTMy+/g1iuawpUa+uGWj231DqFfI3mgpUp0COTjpH0bLlccBqJQ7K9XzpfAyU/FaKrXc71IplQ7YCDgDl4HswCigAdAYOBO/7rwVMJi4Wf8ooLumaceUUv9qmpYx/lyjgH81TZscv/2vpmkZlVKdgIbErU8vCKzQNG30q3K9j2UtKalgtD6lI7yTqxYxr6+Uys0MfbNZptTKSpeaXsO/naTWzaYlWa1tUzrCO/G5uDalIwigRsnOKR3hrT2NjUrpCO/s4JDiKR3hnWX4cXaqejDtlbe1Wcdo0/1WpUh7U82zrqZpT4kbiCe2HxiYoN5qYHUSx2dM8PWol5UBoZqm9XjHuEIIIYQQQrx3qWZwLoQQQgghxJvSUnRluPn8Xw3ONU1bDCxO4RhCCCGEEEIk6f9qcC6EEEIIIT4MKflXPM0p1dxKUQghhBBCiP93MnMuhBBCCCHSnJT8K57mJINzIYQQQgiR5nyYQ3NZ1iKEEEIIIUSqITPnQgghhBAizflQl7XIzLkQQgghhBCphMycCyGEEEKINEdupSiEEEIIIYQwK5k5F0IIIYQQaY4ma86FEEIIIYQQ5iQz50IIIYQQIs35UNecy+D8NYJ5ltIR3kloGu/hD+HCU6iUjvBOrC2sUjrCO3sUFZnSEd7JvvyZUjrCO6la4puUjvDODp1dmNIR3tl+7wUpHeGdtCn7U0pHeCe2fTeldIR3Fv3j7JSO8H8hjQ/dhBBCCPOrUbJzSkd4J2l9YC5EUmTNuRBCCCGEEMKsZOZcCCGEEEKkOR/C0tekyMy5EEIIIYQQqYTMnAshhBBCiDQnVpM150IIIYQQQggzkplzIYQQQgiR5nyY8+Yycy6EEEIIIUSqITPnQgghhBAizYn9QOfOZeZcCCGEEEKIVEJmzoUQQgghRJrzof6FUBmcCyGEEEKINEf+CJEQQgghhBDCrGTmXAghhBBCpDnygVAhhBBCCCGEWcnMuRBCCCGESHM+1A+Eysy5GRWvXoqJe2bwy/6ZNOrWzKS83rduTNg1jXHbpjBg+UiyOmc3KrfOmJ5px+bRfnTn5Ips5NPqpZiwZwYT98+kYRL5Xb51Y9yuaYzZNoX+ifL/eW0No90nM9p9Mj3nD0rO2EbSYh/UqVuNM1578D63jz59u5qUW1lZsWTp73if28e+AxvIndsZgLLlSnLk2FaOHNvK0WPuuDV2MRwze84kbvid5MTJ7WbPX7N2FQ6d3MqRM9vp8ZPpz83KypI5C3/jyJntbN29ipy5nQxlRYoVZvPOFew/+g97D28kXTorACwtLfl12ig8Trlz6MQWGjWua9Y21KlbjdOeu/E6u5feL+mDRUtm4HV2L3v3//2iD8qWwOPoFjyObuHwsa24urkYHafT6Th0ZDNr1i0wa/6E0lUsj/2aJTisW4ZNhzYm5Rka1cNx+9/kWDaPHMvmkaFxQ0NZph7fYb9yIfarFpGpT49ky5xQxRoVWH1oKWsPL6d9j7Ym5aU+K8GSHfPwuLWHmo2qG5Udvr2HpbsWsHTXAn5dPD65Ipv4rEZ5Vh5cwmqPZbTrbtoHJT8rwcLtczlwcxc1GlUzKrN3ysHUFb+wfP8i/tq3EIec9skV+40NmzCFao1a07Sd6bWSGpSqXobpe2fz+4G5NO3W3KTctXMTpu6eyW/bZzByxViyxT8P5C2aj/EbfmHqrriySq5VzJ61nksNLpw/yCUfDwb0725SbmVlxYrlf3DJx4MjHpvJkyenoWzggB5c8vHgwvmDuNR9cS3Mn/cbgf7eeHnuMTpXiRJF8Tj4D55ndrNxw2JsbDKar2HiP0lzM+dKqbzAFk3TPk3hKK+kdDo6jOnCL+3GEBZ8j1H/TMJz10kCr/ob6tz0ucEotwE8i3xGrXb1aDW4PbN7TDGUN+/bhkvHfVIiPkqno/2YLkyOzz/in0l4Jcp/y+cGY+Lz12xXj5aD2/NHfP5nkc8Y2bBfimR/Li32gU6nY8rUMTR2bU9AQDAHD23CfetuLl26aqjTsVNLIiLuU7J4TVq0cGXsuEF07PAjPhcuU7VyY2JiYrB3yM6xY+64b91DTEwMy5etZ+6cpcyf/5vZ80+YPIxWTTsTFBjCtn2r2bltH76XrxnqtGnfnPsRD6hUpj5NvmjAsFF96fpNX/R6PTPnTeLH7wfhc/4yWbJkIioqGoBe/b7n7p0wqpRriFKKLFkymbUNv00ZTRO3DgQEBLP/0Ebct+7mcoI+6NCxJRERDyhVohbNW7gyeuxAvu7YEx8fX6pXaWLogyPHtrLNPa4PALp1/xrfy9eS70lQpyNL/17c+bE/MaF3yLH4D54cOkL0jZtG1Z7s3k/E5BlG+6yKF8OqxKeEfBX3Aiv7vOmkK1OSp2e8kyc7cX3Rb0IverbuR2jQHRa5z+HQjsP4XXmRPyQglLE/TaRt11Ymxz+NfEaHuikzufGcTqej7/he/NSmP6FBd1jg/gceO48kakMI43tPok3XlibHD5s+iKUzlnPy0GnSZ7AmNjb1zRQ2bViXts0bM2Ts5JSOYkKn09F57PeM+WoEYcH3mPjPb5zafQL/K7cNdW5cuM5A1z48i3yGS7sGtB/ciak9fuXpk6f83nsqwX5BZMlhxy9bp+B10JPHDx6ZLeuM6eOp37AN/v5BHDvqzuYtO7l48YqhzjdftyE8/D6fFK1Cy5aN+XnCUNp+1Y0iRQrRsmUTSpSqhZOTPTu2raJIsarExsaydOkaZs9exKJF042+39w5vzJw4FgOHjpGp46t6Ne3GyNH/WqWtpmL3K1F/Cf5SxUk5GYwd26HEBMVzfHNHpRxKW9U59LR8zyLfAbAVU9f7ByyGsryfpof22yZOH8o+Z4IE8pfqiChCfKf2OxB6Vfkv+bpS5YE+VODtNgH5cqV5Pq1m/j53SYqKop16zbTyNV4lrhRo7os/2s9ABs2bKNGjUoAPHkSaRgEWqdLh5bgOfzw4ROEh0WYPX/pssXxu36LWzf9iYqKYtP6bdRrWMuoTv2GtVizciMAWzbtpGr1igBUr1WZi+d98Tl/GYDw8PvExsY99Lb+qhkzps4HQNM0wszYlnLlSnL9+os+WL9ui2kfuNZh5fK4Ptj4hn3g5ORAvfo1WbJ4tdmyJ2ZV9BOi/QOICQyC6Gie7NpL+mqV3uxgTUOlswJLC5SlJcrCgpiwcPMGTqRo6U/w9wsg8FYQ0VHR7Nq0l2r1KhvVCfIP5urF62ipcNAKUCRRG/Zs2kvVesZ9EOwfwrWL19FijYcaeQvlQW+h5+Sh0wA8eRzJ08inyZb9TZUrVZxMtjYpHSNJBUsVItgviNDbIURHRXN48yHK1/3MqM6Fo+cMzwNXPC+T1TEbAEE3Agn2CwIgPDSM+3fvY2tna7asFcqX5to1P27cuEVUVBRr1myisVs9ozqN3VxYtmwtAOvXb6VWzSrx++uxZs0mnj17hp/fba5d86NC+dIAHPI4Tli46WPmx4ULcPDQMQB27zlEs2YNTeqIlJFWB+d6pdR8pdQFpdROpVR6pdR+pVQ5AKVUNqWUX/zXnZRSG5VSm5VSN5RSPZRSfZRSnkqpY0opO3MEzGJvR1jgXcN2WFAYWexfPnit3rI2Z/efIT4zrYd1ZPWEpeaI9kb+a/5qLWtzLj4/gGU6K0b8M4lhG36mtEsFs2Z9mbTYB05ODvgHBBm2AwKCcXJySFTH3lAnJiaG+w8ekjVrFgDKlS/FyVM7OH5yO716DTUMFJOLg6M9AQHBhu2gwGAcHHOY1AmMrxMTE8ODBw+xs8tMgYJ50NBYuX4eOw+s44ee3wBgmynuSX/g0B/ZeWAd8xZPJVt2870QdHRywN//RR8EBgTh5GifqI69oY6hDc/7oFxJjp/cztET2/ip5zBDH0z8ZTgjhk40vOBIDvoc2YgJCTVsx4TeRZ89u0m99DWrkuOv+dj9PBJ9jrjyZ+d9eHraC6et63B0X0vksZNE+91KtuwA2R2yExp4x7AdGnSH7I6m+V/GKp0Vi7bNZcHm2VSrb/4lCUnJ7pCN0MAXfRAadJfsDm/Whlz5c/Lvg3+ZMH80i3bMpfuw79Hp0urTdsqwc8jK3aAXzwP3gu4aTcIkVqtVXTz3nzbZX7BkISysLAi5GZzEUe+Hk7MDt/0DDdv+AUGmj/8J6sTExHD//gOyZs2Ck1MSxzobH5vYhQuXcYtfeteiuSu5cjq9sn5qpGmaWf+llLR6lRcCZmmaVgyIAEwXkRn7FGgLVADGA481TSsNHAU6JK6slPpOKXVKKXXK9+GNtwqolDLZ97KOrtS0GnlLFMB93iYAarevz9l9ZwgLuvdW3/u9+A/5P4/Pvy0+P0C/St8zpvFA5vacRtsRX5M9d/Kvk0yLffAmmV9V59RJL8qXq0f1qk3o2+8Hw5rt5JJktjepo2no9RZUqFiG7l0G0KR+Oxq41qFKtYpY6PU453Tk5HFPXKq34PRJL0aO62+mFiT5q2/aByRZCYBTp7z5rHx9alRrSt9+3UiXzor69Wtx9849vLzOmyPyK7w853ORh44S1LQtoe268PTEGbKMjPuMiD6nExZ5cxPk1pIg15akK1caq1IlkiO0QVJ9kTj/qzQt35KvG3zPiO5j6T26B855kn/w8V8ehxLTW+gpWaE4M8fOoXPDbjjldqRhy3qvP1AYJHWtvuznX7VZDQoUL8imuX8b7c+cIws/Tu3NrH4zzDpge/vH/7f7Pev8XR9+6NqJ48e2YWPzEc+eRf3HxMJc0tya83g3NE3ziv/6NJD3NfX3aZr2EHiolLoPbI7ffw4webbRNG0eMA+gY97mb3UlhgXfw84pm2HbztGOiNAwk3pFK5fArUdzJrQaTvSzuPW1BcoU5uPyRajVvj7WGayxsLQg8nEkayf99TZR3kr4f8jv2qM5ExPkB4gIjXv7+87tEC4du0CeYvm4cyvE/METSIt9EBAQRE5nR8O2s7MDQUEhieoEk9PZkcCAYPR6PZlsbUyWeVy+fI3Hjx5TtNjHeJ45Z9bMCQUFBuOcYLbG0cmBkKBQkzpOzg4EBYag1+uxtbUhPPw+QYHBHD180tCWvbsOUrxkUTwOHuPxo8e4b94NwOaNO2jT7nWvx99eYEAwOXO+6AMnZ0eCgo3bEBgYVycwMNjQhsR94Hv5Go8ePaZo0Y/57POyNGhUm7r1amBtnQ4bm4zM/3MKXb7tY7Z2AMSE3kFv/+KdC32ObMTcvWtUJ/bBA8PXjzZtJVOPLgCkr1GVZ+d90J5EAhB59ARWnxbhmddZs2ZOKDToDjmcXswy53DMzp3gu684wtjdkLgX14G3gjhzxIvCnxYi4Gbga456v+La8KIPcjhm427Im7XhTtAdfM9fJfBW3Ls0B3ccpliZIrBqm1myfojuBd8lm+OL54GsjtkIDzF9HiheuSTNe3zJiJZDjJ7L0mdMz5BFI1g1eTlXPC+bNWuAf5DR7HVOZ0fTx//4OgEBQXGP/5lsCQsLJyAgiWMDX/2ce/nyNRo0ivuQdaFC+WnYoPZ7bE3ySA33OVdK1QemA3pggaZpExOV9wE6A9HAHeAbTdNumpwogbQ6c55w0V0McS8yonnRHutX1I9NsB2LmV6g3PC+in1eR7LlzIHe0oLP3KrgueuUUZ3cxfLx9YTvmdZ5Ig/vvXiCnPvTdPpU7kq/Kt1YNWEph/8+kKwD8+f5cyTIX+El+TtO+J4ZifJnsP0IC6u4H2vGLDYUKvsJgVf8SW5psQ9Onz5LgYJ5yZMnJ5aWlrRo4Yb71t1Gddzdd/NV/OC0WbMGHDhwFIA8eXKi1+sByJXLmUKF83PrZvL+3L3OnCdfgTzkyuOMpaUlTZo3YMe2fUZ1dmzbR8s2TQFwbeKCx8HjAOzfc5iixT4mfXpr9Ho9FSuXx/dy3Icwd27fT6WqccujqlSvaPQB0/ft9Omz5C/wog+at3A17YOte2jzVVwfNH1pHzhRqHB+bt7yZ/TIXylSuDLFi1bj6449OXjgqNkH5gDPLl7CIpczekcHsLAgfd1aPDl41KiOLuuLlX3WVSsRFb90JSY4hHSlS4JeB3o96UqXTPZlLRe9LpMrX04cczlgYWlB3Sa1OLTzyBsda5MpI5ZWlgBksstEifKfcsPXz4xpk3bJ6xI58zkb2lC7SS08dh59/YHEtd8msw2Z7eI+AF22cmn8fF/5nC4Suep9Bcd8TuTIZY+FpQWV3apyctdxozr5iuXn+59/YOK343hw775hv4WlBQPmDeHA+n0cdT9s9qwnT3lRsGA+8ubNhaWlJS1bNmHzlp1GdTZv2Un79l8C0Lx5I/btP2zY37JlE6ysrMibNxcFC+bjxEnPV36/7PHLA5VSDBnci7nzlpmhVR82pZQemAU0AIoCbZRSRRNV8wTKaZpWAlgH/PK686bVmfOk+AFlgRNAi5SNArExsSwbsYD+S4ej0+s4uGYvAVdu06x3a/zOXcVz9ylaD+5AugzWdJ/dF4CwgLtM6zLxNWdOHrExsSwfsYC+8fkPrdlL4JXbNI3P77X7FC3j8/8Qn/9ewF1mdJmIU8GcdJzwPbGahk4ptv6xwegOKcnZhrTWBzExMfTtM5KN/yxFr9exbOlaLl68wrDhvTlz5hzuW3ezZPFqFvw5Fe9z+wgPv0+nDj8C8Hml8vTt25Wo6GhiY2Pp/dNw7t2Lewdj0eLpVK1WkaxZs3D5yhHGj5vG0iVrzJJ/SP/xrFw/H71ex6q/NuB76Sr9h/TA2/MCO7ftY+Wy9fw+dxJHzmwnIjyCrt/E3dXn/v0HzJ21hG1716BpGnt2HWTPzoMAjB81hd/nTmTMz4O4dzec3t2HvvfsCdvQv+8oNmxaYuiDSxevMHTYT5w5c45t7ntYumQ18xZMwevsXsLD7/N1x54AfF6pHL37vOiDPj+NIOxe8n6I0rgxsURM/p1sMyahdHoebd5G9A0/bL/rxLOLvkQeOkLGVl+QvmoltJgYYh88IHzMJACe7D1IunKlsV/+J6ARefQkkR5vNqh8b/FjYpg8dDrTV/yKTq9jy6pt3PD1o0v/r7nkfZlDO49QpOTHTPpzHDaZM1Kl7ud06deJtjW/Jm+hPAyc1BctNhal07F01gqjO6QkXxtimTrsd6asmIRep2fL6rg2dO7XiUvevnjsOsInJT/m5z/HYJMpI5Xrfk7nvp1oV+sbYmNjmTVmDtNXT0YpxeVzvvyzYmuyt+F1+o+cyEnPs0REPKB203b88G17mruljuU3sTGxLBgxl2FLR6HT69i7Zjf+V27Tqk9brp29yqndJ2g/pBPWGdLTd/ZAAO4G3mFS5/F87lqFIhWKkTGzDTVaxH2wfVa/6fj5vN1y19eJiYmh10/DcN+6Ar1Ox+Ilq/Hx8WXUyH6cOu3Nli27WLhoFUsWz+CSjwfh4RG0bfcDAD4+vqxbt5lz3vuIjomhZ6+hhs+3/LVsFtWrfU62bHb4XT/F6DGTWbR4Fa1bNaVbt04AbNzozuIlyfdh9fclFdytpQJwVdO06wBKqVVAE8BwmzdN0xLOUB0D2r3upColF7y/jcS3UlRK9QMyAquANcC/wF6gnaZpeZVSnYh7xdIjvr5f/PbdxGVJedtlLalFWn1r5LlUcOG9s/WhZ15fKRWzsUqf0hHe2aOoyJSO8E58Ps2b0hHeSYvbaf9K1qu0/Wi63zv57q1vLm3K/pTSEd7JxiDTD5qmNdHPApL6JEiKccvtatYx2pbbW78Hvkuwa1780mcAlFItgPqapnWO324PfPaycaVSaiYQrGnauFd93zQ3c65pmh9xH/B8vp3wxqoJ148Piy9fDCxOUD9vgq+NyoQQQgghRNpg7r8QmvAziC+R5MfWk6yoVDugHFA9qfKE0tzgXAghhBBCiFTAH8iVYDsnYPKpc6VUHWAoUF3TtNf+sQIZnAshhBBCiDQnFdyt5SRQSCmVDwgAWhN3624DpVRpYC5xy19CTU9hKm0vohNCCCGEECIFaJoWDfQAdgAXgTWapl1QSo1RSjWOr/YrcZ+NXKuU8lJK/fO688rMuRBCCCGESHNSw01NNE1zB9wT7RuR4Os6//WcMnMuhBBCCCFEKiEz50IIIYQQIs1J+zdpTZoMzoUQQgghRJpj7lspphRZ1iKEEEIIIUQqITPnQgghhBAizUkFt1I0C5k5F0IIIYQQIpWQmXMhhBBCCJHmpIZbKZqDzJwLIYQQQgiRSsjMuRBCCCGESHNkzbkQQgghhBDCrGTm/DVi0/p6JqVSOsH/vcjoZykd4Z1ktLJO6Qjv7GlMVEpHeCd1b/yb0hHeiV6l/Xkga51lSkf4v7fy9LSUjvDO+pYbnNIRPigf6n3OZXAuhBBCfODalP0ppSO8kw9hYC7Em5LBuRBCCCGESHPS/OqGl0j77zUKIYQQQgjxgZCZcyGEEEIIkeZ8mPPmMnMuhBBCCCFEqiEz50IIIYQQIs2R+5wLIYQQQgghzEpmzoUQQgghRJojM+dCCCGEEEIIs5KZcyGEEEIIkeZocp9zIYQQQgghhDnJzLkQQgghhEhzPtQ15zI4F0IIIYQQaY72gQ7OZVmLEEIIIYQQqYTMnAshhBBCiDRHPhAq/rPi1Uvzy97fmXxgFq7dmpmU1+/sxsTd0xm/fQqDVowiq3N2o3LrjOmZfnw+HcZ0Tq7IRopXL8XEPTP4Zf9MGiWRv963bkzYNY1x26YwYPnIJPNPOzaP9qNTJj+k7jbUc6nBhfMHueTjwYD+3U3KraysWLH8Dy75eHDEYzN58uQ0lA0c0INLPh5cOH8Ql7rVX3vO/Xv/5tTJnZw6uZNbfqdZv+5PANzcXDhzehenTu7k2FF3Klcq/87tqlm7Ch4n3Tl6Zjs9fjL9uVlZWTJ34RSOntmO++5V5MrtZCgrUqwwW3au5MDRzew7vIl06axIn96av1bP4dCJrRw4upmhI/u8c8bXcalbg3Nn9+Nz4RD9+v2QRBus+GvZbHwuHOLQwX8MfWNnl5kdO1Zz7+4lpk0da6ifPr01Gzcs5qz3PjzP7Gbc2EFmb8NzVWpWxP3IWrYfX0/nHzuYlJerWJr1u5dyLvAILq61jMrmrZrO8St7+OOvKckV10TlmhXZfHg17seLyuD8AAAgAElEQVTW8u2P7U3Ky1YsxZpdS/AK8KCua02jsjkrp3LEdxez/pqcXHGT9HnNCqw/tJwNR1bSscdXJuWlK5bkr51/cuz2Pmo3qmFS/lHGDLif+ZsB439KhrSmSlUvw/S9s/n9wFyadmtuUu7auQlTd8/kt+0zGLliLNniH0fzFs3H+A2/MHVXXFkl1yrJHf2NDJswhWqNWtO0XdeUjvJSRaqXZOieqQzfP5063ZqYlNf8thFDdv3GwG2/0H35MLI4ZzOUNR70FYN3TmbI7ik0H9kpGVOLt/XeB+dKKXelVOb/UD+vUur8+87xht/7X7OdW6ej49gu/NpxHAPr9OLzxlVxKpTTqM7NCzcY4dqfofX7cNL9KK0HGz9xtujbhkvHL5gr4ispnY4OY7rwW6fxDK77ExUbV8GpYKL8PjcY5TaAYQ36cGrbMVoNNn7ibN63DZeO+yRnbCOpuQ06nY4Z08fj6taO4iVr0qpVU4oUKWRU55uv2xAefp9PilZh2oz5/DxhKABFihSiZcsmlChVi0auX/H7jAnodLpXnrNGrS8oV96FcuVdOHb8NBs2bgNg714PypStS7nyLnT5ri9z577bIEan0/Hz5OG0bfEd1T5zo1mLRhT+uIBRnbbtWxARcZ/Py9Rn7uylDBvVDwC9Xs+seb8woM8oqn/uxheuHYmKigbgj5kLqVqhEXWqfUH5z0pTq07Vd8r5ujZMnz6Oxk06ULJULVq1bMInnxj3zdedWhMREUHRYlWZ8fsCxo8bAkBk5FNGj57MoEHjTM47ddpcSpSsSYXPGvB5pfLUc6lhtjYkbMvwSQP4rk0v3Kq0otEX9ShQOJ9RncCAYAb3HMPWv3eaHL9w1l8M7D7S7DlfRqfTMWxiP7q17U3jqm1o2MyF/IXzGtUJCghhWK+xuCeRf9Hs5QzuMTqZ0iZNp9MxcEIfen7Vjy+rt6de0zrkS9SGYP8QRvWawI4Nu5M8R9eBnTlz1CsZ0prS6XR0Hvs94zuOpned7lRpXI2chXIZ1blx4ToDXfvQt35Pjrofof3gTgA8ffKU33tPpXfdHozrMIqvR3Ymg+1HKdCKV2vasC5zpphes6mF0im+HPMNczr9zIS6fSjbuDIOBZ2N6vj7+PGr22AmNRiA97bjNBkc9yIwX5nC5C/3MRPr9+dnl77kLlmAghWLpkQzzCIWzaz/Usp7H5xrmtZQ07SI933etKZAqYKE+AVx53YIMVHRHNvsQdm6FYzqXDx6nmeRzwC46umLnWNWQ1neT/OTKVtmzh/0Ttbcz+UvVZCQm8GG/Mc3e1DGxXhW9VLi/A7G+W2zZeL8oZTJD6m7DRXKl+baNT9u3LhFVFQUa9ZsorFbPaM6jd1cWLZsLQDr12+lVs0q8fvrsWbNJp49e4af322uXfOjQvnSb3TOjBk/omaNymzatB2AR48eG8o+ypDhnd8iLF22BDeu3+LWTX+ioqLYuN6deg2NZ2PrNazFmpWbANiyaQdVqlcEoEatyvicv4zP+csAhIdHEBsby5MnkRw+dAKAqKgozp31wdHJ4Z1yvkr58qWMf45r/8HNzcWojpubC8v+WgfA339vpWbNygA8fvyEI0dOEvn0qVH9J08iOXDgqKENXp7ncM7paLY2PFeiTDFu3fDH/2YgUVHRuG/YSa361YzqBN4OwtfnKrGxsSbHHzt0kkf/PjbZn1yKlylqyB8dFc22jbtekd/0d/f4oVM8TsH8AMVKF+G2XwABt4KIjopm56Y9VK9nPIMc5B/M1YvXkmzDJyUKkzWbHccOnEyuyEYKlipEsF8QobdDiI6K5vDmQ5Sv+5lRnQtHzxkeR694XiarY9ysbdCNQIL9ggAIDw3j/t372NrZJm8D3kC5UsXJZGuT0jFeKk+pgty5GcK926HERMVwZvMRiid6Lrty9AJR8X3g53mFzPHPZRoalukssbC0wMLKEr2Fnod37id7G8R/858H50qpAUqpnvFfT1VK7Y3/urZS6i+llJ9SKlv8jPhFpdR8pdQFpdROpVT6+LpllVLeSqmjQPcE5y6mlDqhlPJSSp1VShWKP88lpdSS+H3rlFIZEpzngFLqtFJqh1LKMX5/AaXU9vj9h5RSn8Tvz6eUOqqUOqmUGosZZXHISljQPcN2WNA9sjjYvbR+9Va1Obv/DPE5aTusEysnLDFnxFfKYm9HWOBdw3ZYUBhZ7LO+tH71lsb5Ww/ryOoJS82e81VScxucnB247R9o2PYPCMIp0YAzYZ2YmBju339A1qxZcHJK4lhnhzc6Z9OmDdi77zAPH75406hJk/qcP3eAfzYtoUuXvu/ULkfHHAQGBBu2gwJDcHS0T1THnsCAIEO7Hj54iJ1dZvIXzIsGrFw/n50H1tO957cm57fNZINL/Zocih/omkPin29AQBDOifvGyQH/BH3z4MFDsmbN8kbnz5TJlkaN6rBv3+H3F/olcjhkJzggxLAdEhSKvWP2VxyRuuRwyE5wYKhhOyQwlBwOaSc/xLUhJOBFG0KD7pDDIdsrjnhBKUXvkT2YPna2ueK9lp1DVu4GvXgcvRd012gSI7Fareriuf+0yf6CJQthYWVByM3gJI4Sr5LZ3o6IwBfjiYige2Syf/njTcWWNfHZH/dOi9+ZK/gevcDYk3MZd2IuFw96E3ItwOyZk4umaWb9l1LeZub8IPD8PeVyQEallCVQBTiUqG4hYJamacWACOD5YrVFQE9N0z5PVL8rMF3TtFLx5/aP3/8xME/TtBLAA+CH+O/5O9BC07SywEJgfHz9ecCP8fv7Ac8f2aYDf2iaVh4w6yOESmLfy/q5UrNq5CtekK1zNwJQu0N9vPedMRrcJzelTFvwsl/USk2rkbdEAdznxc2G1m5fn7MpnB9SdxveJFvSdV5+7Jucs3XLJqxavdFo36ZN2/m0eHWat/iW0aP6v1H+l0kyA2/WLgu9ns8qlqF7l/40qf8VDVzrUKVaRUMdvV7PnAWTWTD3L27d9Dc5x/vyZn1jetybPJDr9XqWLZ3JrFmLuHHj1ltnfFMv+1mnFUn/PqUxSf6uvNmhX3ZqxuE9xwhJ8AIluakkGvCy3/WqzWpQoHhBNs3922h/5hxZ+HFqb2b1m/HBfoDPrP7DdVyuaRVylyjA3nn/AJAtjz0OBZ0ZUbEbwyt2pXClTylQoYg504r34G3u1nIaKKuUsgGeAmeIG0hXBXoCgxPUvaFpmleC4/IqpTIBmTVNOxC/fxnQIP7ro8BQpVRO4G9N067EPzjf1jTt+TTTX/HfZzvwKbArvo4eCFJKZQQqAWsTPLCni/+/Mi9eICwDJiXVQKXUd8B3AJ/ZlaJQxnxJVXulsOB7RstU7ByzEhESZlKvWOUSNO7RggkthxP9LG59baEyH1O4fBFqt6+P9UfWWFhaEPkokjWT/vrPOd5WWPA97JxezO7YOdoREWqav2jlErj1aM6EVi/yFyhTmI/LF6FW+/pYZ4jP/ziStcmYH1J3GwL8g8iV88UHIXM6OxIUFJJknYCAIPR6PZky2RIWFk5AQBLHBsYd+6pz2tlloXz50jT/MukPtx7yOE7+/HnImjUL9+6Fv1W7AgNDcHJ+Mcvs6GRPcFBoojrBOMVn1uv12NjaEB4eQWBgCEcPnyQsLG5V3J5dBylRsigeB48BMHn6aK5fv8n8P8z7jkzin6+zsyOBifsmIJicOZ0ICAhGr9dja2tjyP0qs2dP4urVG/w+88/3njspIUGhODi/eOfC3jEHocF3kuV7vw8hQaE4OOUwbNs75eBOGsoPcTPl9s4v2pDDMTt3Qu6+4ogXipcrRunPStKiU1MyfJQeC0tLHj96wswJc80V18S94Ltkc3zxOJrVMRvhSTyXFa9ckuY9vmREyyGGx1GA9BnTM2TRCFZNXs4Vz8vJkvlDExF8j8xOL8YTmR2z8iDU9DG6cOXiuPT4ghmtRhn6oES9Cvh5XuHZ47ildhf3e5G3dCGunbiYPOHN7EP9I0T/eeZc07QowA/4GjhC3Gx5TaAAkLi3Ey68jCHuxYDiJZMfmqatABoDT4AdSqnni1UT19fiz3NB07RS8f+Ka5rmEt+miAT7S2maViTRsa9r4zxN08ppmlbubQbmANe9r+KQz5HsuXKgt7SgolsVzuwyXjOYp1g+vv65K1O//ZkH916sAfuj1zR6V/qePlW6snL8Ejz+3p+sA3OAG95Xsc/rSLaccfk/c6uC565TRnVyF8vH1xO+Z1rniTy898Cwf+5P0+lTuSv9qnRj1YSlHP77QLIPzCF1t+HkKS8KFsxH3ry5sLS0pGXLJmzeYvyBts1bdtK+/ZcANG/eiH37Dxv2t2zZBCsrK/LmzUXBgvk4cdLzteds0dyVre67eZpgPXSBAnkNX5cu9SlWVpZvPTAH8DpzjvwF8pA7jzOWlpY0bd6Qndv2GdXZuW0fLdvE3W3AtUk9DscPvvfv8aBIsY9Jn94avV7P55XL43v5GgADh/bCxtaG4YN+futsb+rUKW8KFsz74uf4ZWO2bNllVGfLll20b9cCgC++aMT+/a9fojJqVH8y2drQt98oc8RO0jlPH/Lkz4VzbicsLS1o2MyFfTsSv8GZep33vEju/Llwzu2IhaUFDZrWTVP5AXy8LpErX06ccsW1waVJbQ7u8HijY4d3H4truRY0rtCSaaNn4752e7IOzAGuel/BMZ8TOXLZY2FpQWW3qpzcddyoTr5i+fn+5x+Y+O04o+cyC0sLBswbwoH1+zjqbv5lXB+qW97XyJ7XAbuc2dFb6injVolziZ7LchbLS+sJnZnf+Rf+TfBcFh54l4KfFUWn16Gz0FPgsyKEXDXfO4/i/Xjb+5wfJG65yDfAOWAKcFrTNC2ptyET0jQtQil1XylVRdM0D8BwXymlVH7guqZpM+K/LgFcB3IrpT7XNO0o0AbwAC4D2Z/vj1/mUljTtAtKqRtKqS81TVur4gKV0DTNGzgMtCZu9t30flbvUWxMLEtHLKD/0hHo9DoOrtlDwJXbfNGnNTfOXsNz90laD+mAdQZrfpwdd7eKe4F3mdrZ/IOPNxEbE8uyEQvov3R4fP69BFy5TbPerfE7dxXP3adoPbgD6TJY03123DrlsIC7TOsyMYWTv5Ca2xATE0Ovn4bhvnUFep2OxUtW4+Pjy6iR/Th12pstW3axcNEqliyewSUfD8LDI2jbLu6Wfj4+vqxbt5lz3vuIjomhZ6+hhg/zJXXO51q1bMwvv84yyvFFs4a0a9eCqKhoIp9E0varbu/criH9x7Fy/QL0eh0r//qby5euMmDIj3h5nmfntn2sWLaOmXMncfTMdiLC7/P9N3E/+/v3HzB31mK2712Lpmns2XWQ3TsP4OhkT+/+XfG9fI1dB9cDsHDeClYsW/dOWV/Vhp9+Gs6WzX+h1+tZvGQ1Fy/6MmJEX86cPsuWrbtYtHgVixZOw+fCIcLCImjf4cVtKy9fPoKtjQ1WVpa4udWjketXPHz4kMGDenLp0hWOH4u7U84fcxazaNEqs7QhYVvGDfqVBatnoNPr+HvFZq5evs6PA7/jvNdF9u04xKelivD74l+wzWRLTZeq/DjgO9yqtQZg2T/zyF8wDxk+Ss8+r80M6z2ew/uOmTVz4vwTBk9m7qrp6PU6NqzcwrXLN+g+oAsXvC+xPz7/tEWTsM1sQw2XKnTv34Wm1dsCsGTTHPLF59/t+Q8jeo/nyP7jr/mu778Nvw6Zyu8rf0Ov1/HPqq1c9/Xj+/7fctH7Egd3HqZoyU/4deF4bDPbULVuJb7r/w2tapje9jIlxMbEsmDEXIYtHYVOr2Pvmt34X7lNqz5tuXb2Kqd2n6D9kE5YZ0hP39kDAbgbeIdJncfzuWsVilQoRsbMNtRoETfXNqvfdPx8bqRkk0z0HzmRk55niYh4QO2m7fjh2/Y0T/Rh+pQUGxPLuhEL+WHpEHR6HcfW7Cf4ij8Ne3/JrXPXOb/7NE0Gt8MqgzVfz+4NQHjAXeZ3+RUv92MUrvQpg3ZMBk3j4gEvzu85k8Iten8+1L8Qqt5m/ZdSqjZxy0oya5r2SCnlC8zRNG2KUsqP+LXowBZN0z6NP6YfkFHTtFFKqedrxB8DO4hbN/6pUmow0A6IIm5NeFvAFnAn7gVBJeAK0F7TtMdKqVLADCATcS80pmmaNl8plQ/4A3AELIFVmqaNid+/Ir7uemCYpmkZX9XW9nm+SNM9r3vNiyVhfssDk28wYw7ZMqS+uyv8V+GRZrtrarLIb2v+O7uYk16l/T+pYa2zTOkI7ySv1Zt9YDm1Wnl6WkpHeC/6lhv8+kqp2Ay/1alqUFHC4XOzjtHOBh9Nkfa+1cy5pml7iBv0Pt8unODrvPFf3iVuTfjz/ZMTfH0aKJnglKPi9/8MGE0dK6VsgVhN00z+OkD8evZqSey/AdR/yf6EH0JNPdO8QgghhBDijcV+oB8wTvvTGUIIIYQQQnwg3nbNebLRNM2PBDPwQgghhBBCfKhrzmXmXAghhBBCiFQi1c+cCyGEEEIIkZisORdCCCGEEEKYlcycCyGEEEKINEfWnAshhBBCCCHMSmbOhRBCCCFEmiNrzoUQQgghhBBmJTPnQgghhBAizflQ15zL4FwIIYQQQqQ5sqxFCCGEEEIIYVYycy6EEEIIIdKcD3VZi8ycCyGEEEIIkUrIzLkQQgghhEhzNC02pSOYhQzOX6Pl03QpHeGdFM4ckdIR3snV8MwpHeGdbbX+KKUjvJN7jx+kdIR3ZqFP2w91X2YolNIR3smg3jYpHeHd6dL2G822fTeldAQB/Hbq55SOINKAtP2MJYQQQogPXt9yg1M6wjuTgfn7FytrzoUQQgghhBDmJDPnQgghhBAizdHkPudCCCGEEEIIc5KZcyGEEEIIkebImnMhhBBCCCGEWcnMuRBCCCGESHNkzbkQQgghhBDCrGTmXAghhBBCpDmxMnMuhBBCCCGEMCeZORdCCCGEEGmOJndrEUIIIYQQQpiTzJwLIYQQQog0R+7WIoQQQgghhDArmTkXQgghhBBpjvyFUPGfZa9Zkpoev1Hr6FQK9mj80nqOrhVwC15JppL5jfand85Kg2uLyN+tkbmjJilDlbLk2zaffDv+xK7Llyblts3qUODIKvJsmEmeDTPJ1KKeUbnuowzkP7CMHMO7JVdkE9lrlqT64d+ocWwqBX58eR84uFagUYhpH1g7Z6Xe9eTtg1p1qnLs9HZOeO2iZ+/vTMqtrCxZsGgaJ7x2sWPvWnLldjYqd87piF+gJ91//Maw77tuHTh0bAsex7fy/Q8d33tmF5canD9/kIs+HvTv3z2JzFYsX/4HF308OOyxmTx5chrKBgzowUUfD86fP0jdutWNjtPpdJw8sYONG5aYnHPa1LGEh/m+97YA1K1bHW/vvZw/f4B+/Ux/f62srFi2bCbnzx/g4MGN5M4d155atapw+PAWTp7cweHDW6hevZLhmFGj+nPlylHu3PExS+aXKVi9BD33/Eqv/b9RtZubSXm5r2rTfftEurlP4Nu1I8heMO73qUCVT+m6eRzdt0+k6+Zx5Pu8aLLmfk6XpyjWHUZh3XEMFuXqmZRbVvsS67ZD4/51GE36rlPijstZ+MX+tkNJ3/139PlLJnd8AA7fvEfTv47SeNkRFp72MymffMiXVquO02rVcZosO0LVeQcMZdMOX6H5imN8sfwokw5eNuvb+PVcanDh/EEu+Xgw4CXX8Yrlf3DJx4Mjia7jgQN6cMnHgwvnD+KS4DqeP+83Av298fLcY3SuEiWK4nHwHzzP7GbjhsXY2GQ0W7sAilQvydA9Uxm+fzp1ujUxKa/5bSOG7PqNgdt+ofvyYWRxzmYoazzoKwbvnMyQ3VNoPrKTWXO+jWETplCtUWuatuua0lGSnaZpZv2XUv4vB+dKqbxKqbZm/SY6RfGfv+Z420nsq9YPp2aVyFjY2aSa/iNr8n1bn/DTV0zKio1uT+heL7PGfCmdDvsR3fHvMpwbrt9j06gGVgVym1R7uO0AN5v14GazHtxft8OoLFuv9jw5eS65EpvSKYpN/JoTbSdxoOqr+yBv56T7oOiY9tzZk3x9oNPpmPTbSFo170Ll8g35ooUrhT8uYFTnqw5fEhFxnwql6jJn1mJGju5vVD7u5yHs2XXQsP1JkUK079gSl5otqF6pMS71apK/QJ73mnnG9PG4ubWjRMmatG7VlCJFChnV+ebrNkSE36dI0SpMnzGfCROGAlCkSCFatWxCyVK1cHX9it9nTECne/Gw1PPHzly8ZNovZcuUIHPmTO+tDYnbM23aWJo06Ujp0nX48svGfPKJcXs6dWpFePh9Pv20Or///ifjxw8C4N69cFq0+Iby5evRpUsfFi6cajjG3X03VauaDgrMSekUrmM6sazTL8ysO4DijT83DL6fO7fpCLPqD+KPhkPwmLuF+sO/AuBR+EOWfzuZWfUH8XffOTSfmgIvspXCqkYbnm6cSeSy0VgULo+yczSqEnVwLZErxhO5YjzR3vuIueoJQKy/r2F/5PqpEP2MmFvJ+8IIICZWY+KBy8x0K8X6thXZ7hvCtbB/jer0q1qY1a0/Y3Xrz2hdIhe1C2QHwCsoAq+g+6xp/Rlr21TkQsgDTgdEmCXn8+vY1a0dxUvWpNVLruPw8Pt8UrQK02bM5+cE13HLlk0oUaoWjRJdx0uXrqGR61cm32/unF8ZMnQCpcvUYePGbfTra77fL6VTfDnmG+Z0+pkJdftQtnFlHBJdB/4+fvzqNphJDQbgve04TQbHZc5XpjD5y33MxPr9+dmlL7lLFqBgxZR5ofoyTRvWZc6UcSkdQ7xH/5eDcyAvYNbBeZbSBXl0I5jHt0LRomII3HgUh3rlTOp9MrAlV2dvJuZplNF+h/rleHQrlIeX/c0Z86WsSxQm6lYgUf7BEBXNQ/cDZKxd8Y2PT1esIPqsWXh0+IwZU75a5jIFeXwjmCc3X/SBfX3TPvh4UEuuz9pMbKRxH9g3KMfjm8nbB2XKleDG9Zvc9LtNVFQUG9ZvpUGjOkZ1GjSqzaqVGwD4Z+N2qtb4PEFZHW763ebypauGfYU/LsDpk948eRJJTEwMRw6foJFr3feWuUL50ly75seNG7eIiopi9ZpNuLkZz3C6ubmwbNlaANav30qtmlXi99dj9ZpNPHv2DD+/21y75keF8qUBcHZ2pEGD2ixcuNLoXDqdjokThzNosHmejMqXL8W1a374xffB2rWbcU3083J1rcvy5esB+Ptvd2rUqAyAt/cFgoJCAfDx8SVdunRYWVkBcOKEJ8HBoWbJ/DI5SxUg7GYI4bfvEBMVw7nNx/jEpaxRnaf/PjF8bZUhHc/fJQ6+cJOHoXEDwVBffyzSWaK3St6VkDr7vGj3Q9Ee3IXYGKJ9T6LPX+Kl9fWFyxPte8p0f6EyxPhdgOioJI4yr/MhD8iVKT05M6XHUq+jXiF79l+/+9L626+EUL+QPQAKxbOYWKJiY3kWE0t0rIZdBiuz5Ex8Ha9Zs4nGia7jxi+5jhu71WPNS67jQx7HCQs3fUHxceECHDx0DIDdew7RrFlDs7QLIE+pgty5GcK926HERMVwZvMRiruUN6pz5egFoiKfAeDneYXMDlmBuFv1WaazxMLSAgsrS/QWeh7euW+2rG+jXKniZLK1SekYKSJW08z6L6V8UINzpVQHpdRZpZS3UmqZUmqxUmqGUuqIUuq6UqpFfNWJQFWllJdSqrc5slg7ZuFJ4D3DdmTQPawdsxjVsf00L+md7Ajd5Wm0X58hHQV6uOE7eb05or0RC/tsRAXdMWxHB9/Fwj6rST2bulXIu2k2TtOHYuEQ/zagUuQY2IU7vy5IrrhJsnZI1AeB97B2MO0D61f0wZVk7gNHR3sC/YMN24GBwTg62ZvUCfAPAiAmJoYHDx5iZ5eFDBnS07N3F36dONOo/kWfK3xeuRxZ7DKTPr01dVyq45TTePbxXTg5O+DvH2jYDggIwtnJwaTO7fg6MTEx3L//gKxZs+DsZHqsk3Pcsb/9NprBg8cRGxtrdK7uP3zNli07zTbQdXJywD/+52toj7NDEnVetOfBg4dkzWr8u9WsWUO8vS/w7Nkzs+R8Ezb2dtxPcA08CArD1j6LSb0K7evy04EpuAxqw9ZRpkuIijaoQNCFm8Q8izZr3sRUxixoD8MN29q/EaiMpvkBlI0dukzZiL19yaTMonA5on1Pmi3nq4Q+isTextqwbZ8xHXcePU2ybuCDJwQ+eEL5nHYAlHTMRDnnLNRd6IHLokNUyp2V/HYfmSVnwmsUwD8gCKc3vI6dnJI4NtE1k9iFC5dxc3MBoEVzV3LldHpfTTGR2d6OiATXQUTQPTIlcR08V7FlTXz2x71j6nfmCr5HLzD25FzGnZjLxYPehFwLMFtWIeADGpwrpYoBQ4FamqaVBHrFFzkCVQBX4gblAIOAQ5qmldI0bWoS5/pOKXVKKXVq++OriYvfNJDpPs24vNiY9lwY/ZdJtY/7t+D6vG3EPE76ATzFJHoR+e++41yv3Qm/Jj/w6IgnDhP7ApC5rSuPDpwkOvjls0PJIqk+SFRedEx7Lo4y7YPC/VtwY27y94FKInPidW9J1kFj4JCezJm1mEePHhuVXfG9xoyp81m/cRFr/v6TC+cuERP9/gZZb51Ze/mxDRvW4U7oXc54Gi+LcnS0p3lzV2bOWviOqV8uyUv3jdrzok6RIoUYN24QPXoMfu/5/os3aQvAiWW7mFa9DzsnrqL6j02NyrIXcsZlUGv+GfKnuWL+Ny+ZzdIXLkf0lTOm5Rls0WV1JvbmhWQI9252XAmhdoEc6HVxHXcr4jE3wh+xo1NldnSqwgn/ME4HhL/mLG/HHNfxq3T+rg8/dO3E8WPbsLH5iGfPzPiuxktyJ6Vc0yrkLlGAvfP+ASBbHnscCjozomI3hlfsSuFKn/9WMosAAB/jSURBVFKgQhHzZRX/yYe65vxDultLLWCdpml3ATRNC4t/wNioaVos4KOUsn/VCZ7TNG0eMA9gs0Obt+qdyMAw0ju9mGm2dsxKZPCLB1WLjNbYfpyLSn+PACBd9kxUWNKPEx0nk7l0QRxdP6Po8LZY2mZAi9WIfRqF38KdbxPlrUSH3MXSMfuLvA7ZiA69Z1QnNuKh4ev7a7eTvV/cBxDTlypC+rLFyNzWFZXBGmVpSeyjSO5OWZQ84eNFBiXqAyfTPrD5JBcVn/dBjkyUW9qPUx0mk7lMQRxcP+OT4W2xzBTXBzFPo7hp5j4IDAzGKeeLGScnJweCg0JN6jjndCQoMAS9Xo+trQ3hYRGUKVcStyb1GDmmP5ky2RKrxRL59Bl/zvuL5cvWsXzZOgCGjuhDYGAw70uAfxA5E8x6OTs7EhgUYlInV04nAgKC0Ov1ZMpkS1hYOP4BpscGBYbg6lYXV1cX6tevhbV1OmxtbViyeAarVm+iQIG8XLp4GIAMGdJz0ceDIkWrvL/2BASTM8E7C87OjgQGJmpPfO6AgGBDH4SFRcTXd2D16nl07tyHGzduvbdcb+NBcBiZElwDto52hqUqSTm/+Shu475mA3Pj6jvY0WZub/7uM4fwW8m7JAdA+zccZfNihlNlzIz2KOn8FoXL8Wz/qiT3x1zzgkTvwCSXHB9ZE/Iw0rAd8u9Tsn+ULsm6O66EMKj6x4btfdfvUNwhExnilxNVzpOVcyEPKOv88lnft/X8Gn0up7MjQW94HQcEJHFsomsmscuXr9GgUdzq0kKF8tOwQe332BpjEcH3yJzgOsjsmJUHoaYvcgpXLo5Ljy+Y0WoU0fHvEpWoVwE/zys8i5+oubjfi7ylC3HtxEWz5RXig5k5BxQmc7sAPE1UJ1lEeF3jo/wOpM+dHWWpx6np5wTvPG0oj374hB3FvmNP+Z7sKd+T8DNXOdFxMve9r3Ok6WjD/uvzt3FlxsZkHZgDRJ7zxTKPE5bO9mBpgU3D6vy795hRHX32F08QGWtV5Nm12wAE9f+F67U6cr12J+78soAHm3Yn+8Ac4L6naR+E7DDug11Fv2Nf+Z7sK9+TiNNXOdUhrg+ONhlt2H9j3jau/a+9Ow+TqjrzOP79dYM7iIhKjCCIgoNrjAou4zbGxETUiLsg0eg8kUSMayAuRBKjcY1xYlxG2dx3BRRXQAUV2WFU3CUYdwERNUL3O3+cW1INBXRVQ5861e/neXg6dauZ+V2s5dxz3/Oeax9a4wNzgKmTZ7LVVh1ov+UWNG/enJ/3/BmjH63b5WD0o89w7HE/B+DQw3/Cc+NeAKDHT45nlx0OYJcdDuDGfwzlr1fewC03hbsCbdqE2+Tf3+J7HHLoQTxw38jVlvnlSdPYeuuOdOjQjubNm3PM0YcxcmTdf6uRI5+gd+/Q8adnz58xZuz4744fc/RhrLXWWnTo0I6tt+7IxJencsEFl9Fxq13ZpnN3TujVlzFjxtPnF/147LGnadf+B2zTuTvbdO7OV199vVoH5gCTJk1n6607suWW4XyOOqoHo0Y9Wed3Ro16ihNO6AnAEUf8lHHjJgCw4YYteeCBwVx00eW88MLytc+N7f3pb9O6Q1tabbEJ1c2r2aFHd157cnKd32ndYemcRecDduazd8OF2zot16PX4HN46vK7mTN5zXTFWZXaj95DrTZFLTeGqmqadd6NmrdnLPd7arUZrLM+tR+8vdxz1RFLWgC226wFcxZ8xftffM3imloef+Mj9uvYZrnfe3feIr749xJ2art0oXPbFusw+f15LKmtZXFNLVP+NZ+OG623RnIu+z4++ujDGLHM+3jECt7HI0Y+wdEF3scrs8kmYbAsid8POIMbbxq+Bs4qmDP9LTbp0JbW2ftglx57MvPJuu/PLbbrwLF/PoWbT7mcLz/74rvj8/71KVt360pVdRVVzarp1O0/+OjNOGvB3PJqsTX6J5ZKmjl/GnhQ0jVm9pmk1iv53YXAGl09YTW1zPr9ELrfOQBVV/HPO8fy5ey5dDnvSOZPe4ePnpi86v8jMdXU8vEf/8EWt/wJqqpZcP8TfPvmHDY+vTffzHqdRWNeYqPeh7HB/t2xmhpqFyzkwwFXxU5dh9XUMmvAEHa/K/w3mJv9N+h83pHMn/4OHz9efv8Nampq6H/uIO598Baqqqu5Y/h9zH7tTfqf349pU2Yx+rFnuH3YvVx/0xVMnPYk8+ct4NSTVr1sYvBt/0Pr1q1YvHgJ5519MQvmf7HKv1NM5jN+ewGjRt1BdVUVQ4bezSuvvM7AgecwefJ0Ro58klsH38WQIX/j1VeeZ968+ZzQqy8QFk3ee98IZkwfw5KaGvqdcf5yNeaNraamhjPPvIgRI4ZRXV3N0KH38Oqrb3DhhWcxZcoMRo16iiFD7ubWW69h1qxxzJs3n969fwPAr37Vh06dOtC//+n07386AD169OaTTz7jkksGcMwxh7Heeuvy5psvMnjwXVxyyV/X6LnU1tQy6qIhnDjsd1RVVzHlnnF88sb7HHBmT96f+Q6zn5pCtz4H0Wmv7alZUsM3CxbxwNk3ANDtxINoveVm7Nvv5+zbL1wMDut9GYs+W32vnVWyWr4dezdrH94PVMWSVyZgn39A8+49qP3oPWreCQP1Zl12o6bAAFwtNkYtWlM7d/mOP42lWVUVv9unC30fnkqtwWFdv0enjTfg+pfeouumLdmvY7hDOfr1j/jxNpvVKRE5sNOmvDz3c46+8yUA9my/Mft23KTg/5+Gyr2PH13mffyHgecwKe99PHTI33gtex8fn/c+vu++Ecws8D6+bfjf2XefPWjTpjXvvj2JiwddyeAhd3HsMYdz2mm/AOChhx5lyNC718h5QXgf3HfRrfQd9nuqqqt48Z6xfPjGXH565lHMmfk2s56azGEDerHWeutw0vXh83Te+59y86lXMO3RF+m85/b0f/xKMOPVcdOY9XS8RgeFnDvwMl6eOoP587/gvw7vRd9f9qZnj+Xbjrp0qJK2PpXUBzgXqAFyl+0jzey+7PkvzWwDSc2B0UAbYEihuvOcUstaykXnVmum7VZjeXNeq9gRGqzP1+V3EVCMBd8sih2hwZpVpz0Pce5mq/fuQGPrf2YFdJKoSvtGc8uzH44doUH6bp72ewDgqkmXxo7QYM3bbNVoFQj10XL9rdboGO2LRW9HOd+0v7GWYWZDgeVbDSx9foPs52JgzRW4Oeecc845V4KKGpw755xzzrmmIWYv8jUp7ft0zjnnnHPOVRCfOXfOOeecc8mxiB1V1iSfOXfOOeecc65M+My5c84555xLjtecO+ecc84559Yonzl3zjnnnHPJqaS9evL5zLlzzjnnnHNlwmfOnXPOOedccrxbi3POOeecc26N8plz55xzzjmXnEqtOffBuXPOOeecS06lDs69rMU555xzzrky4TPnzjnnnHMuOZU5b+4z584555xzzpUNVWq9Tiok/beZ3RQ7R0Okfg6eP77UzyH1/JD+OXj++FI/B8/vyoXPnMf337EDrAapn4Pnjy/1c0g9P6R/Dp4/vtTPwfO7suCDc+ecc84558qED86dc84555wrEz44j68S6sNSPwfPH1/q55B6fkj/HDx/fKmfg+d3ZcEXhDrnnHPOOVcmfObcOeecc865MuGDc+ecc84558qED86dc84555wrEz44j0DSGfU55tyKSGodO0NDpJ4fQNKVkraLnaNUkv5Sn2PlTFJbSYdK6iGpbew8pZC0i6R+kk6XtEvsPC4tPp6oTL4gNAJJU8xsl2WOTTWzH8TKVCxJ3we2BJrljpnZs/ESFU/SnkAH6p7DsGiBiiDpDWAaMBh4zBJ7I6eeH0DSKcBJhNfPYOBOM1sQN1X9reBzaIaZ7RgrUzGyf/+LgGcAAfsCg8zs1qjBiiDpIuAo4IHs0OHAvWb2p3ip6k9SK+BElv8c7RcrU31IWgis8DPHzFo2YpwGqYTxhFueD84bkaTjgOOBvYHn8p5qAdSY2YFRghUpm107BngFqMkOm5kdGi9VcSQNBzoRBoj551DWXyo5kgQcCJwM7A7cDQwxs9ejBqun1PPnk9SFMEg/DhgP3GxmY+KmWjFJpwF9ga2At/KeagGMN7NeUYIVSdJsYE8z+yx7vDEwwcy6xE1Wf5JeBX5gZt9kj9cFppjZf8RNVj+SJgAvAjOB2txxMxsaLVQRJA0CPgSGEy7wTgBamNnlUYPVQ6WMJ1xhPjhvRJK2BDoClwL9855aCMwwsyVRghUp+1Lc0cz+HTtLqbIvxa4pztguS9L+wG3A+sB0oL+ZvRA3Vf2lnF9SNXAIYXDeDriH8GW5yMyOjZltRSRtCGxEgc8hM/s8TqriSXoaONjMvs0erwU8mtKgRNJjwHFmNj973Aq4zcwOiZusfgrN2qZE0ktm1m1Vx8pRpYwnXGE+OHdFy75QjjKzL2NnKZWke4F+ZvZB7CylyGYJewG9gY+AW4BHgJ0Jt8U7Roy3SqnnB5B0NXAo8DRwi5lNzHtudrnO4EpqaWZfrKjuP5UBuqRhwA7Aw4QShcOAicDrAGZ2dbx09SPpIWA34EnCOfwIeB74GJIoDzkT+BIYCXw3WZPQa2gC8HfgLsK//3HAr81sz6jBXJPXbNW/4lY3SUcAfwE2JdxKE6GkIpU6t6+AadnMVf4Hcll/kQBIGkH4EG4BvCJpInXPIZXSnBcIt2IPN7O5eccnSbohUqZipJ4fYBZwgZl9VeC53Rs7TBHuIMz2Tya8F5T3nBHKXVLwFnXLch7OfraIkKVUD2Z/csZGylGqb4ErgPNZWsOd0mvoeODa7I8RytKOj5qoSBUwnnAF+Mx5BJLeBHqY2auxs5RCUp9Cx1OoM5S078qeN7NxjZWlISQp5ZKc1PPnSNoI2AZYJ3cstYXRzpVK0ltANzP7NHaWpir18YQrzGfO4/go5TeSmQ3N6js7Z4dmm9nimJnqKzf4lvQXM/td/nPZQtckBudAG0nnAdtRd2B4QLxIRUk9f65byBnAFoSFxd0JdwSSOAdJewHTzGyRpF7ALsBfzWxO5Gj1ImlXwoztsl2jkug2AyDpEOCPLD2H1GY9/49wJzVJkjoD/wA2M7PtJe0IHJpKt5xM0uMJV5jPnEcg6VqgLfAQdUsqHljhXyojkvYDhgLvEr5M2gF9UpoxrIA2ck8QOpycA/wK6AN8suwFR7lKPT+ApJmEeuEXzWxnSdsCF5vZMZGj1YukGcBOwI6EEqNbgCPMbKV3l8pFtjD9XJbvFPJetFBFymY9jwBmpngnSdKDhAvsMSRW4gggaRzhNXRjrvWgpFlmtn3cZPWX+njCFeYz53G0JMw2HJR3zFja67bcXQUcZGaz4bvZhzuBH0ZNVQ/5beSywUlOC2BCnFQl2djMbpF0RnY3YFz2RZOK1PMDfGNm30hC0tpm9lrWVjEVS8zMJB0GXJv99yhYslamPjGzR2KHaKB/ArNSHJhnHsr+pGo9M5sYOrt+J7UuJ6mPJ1wBPjiPwMxOip2hgZrnBuYAZva6pOYxAxXhDuAxEm8jB+TKiD6Q9DPgX4TyilSknh9gbtb67iHgSUnzCOeRioWSBhC65uyTtYVM5X0MMFDS/xK65aQ6Y3ge8Gh2YZp/DmXfaQbSWGe0Cp9K6kS2mFXSkUBqHbyqgDPy2nFuRJhAcwnzspYIJK0D/JLl621PjhaqCJJuJXyYDc8O9QKqU7voyAYjm1G3XjWVettDCBtPtAOuI8yeXJzKTGLq+ZeVLTTeEBid67td7hS2uz8eeNnMnpPUHtjP0tkl9zZgW0Ldc66sxVL5HIXvyru+ZPnSnIujhSqCpHcosNOmmSXRrUXSVsBNwJ7APOAdoJeZvRszVzEK7QZa6JhLiw/OI8h6bL9G+GIcRNiV7FUzOyNqsHqStDbwa8JmKwKeBa5PaVMiSb8B/kDosZ3/xZ5EzbmLZ0X9wXNSuAOTXZg+ntKGPcuSNNPMdoidoyEkTTKzXWPnKFW2X0HOOsBRQGszuyhSpJJIWh+oMrOFsbMUS9J0wkX1vOxxa2Bc6u+Nps4H5xHkrmpzCxCzkpDHU+pUkZN9EGxhZjNW+ctlJFuI1c2yrb9TIek6CsxU5ZT7QqzU80Od2UIB7QkzbgJaAXNS2EAJQNIjQG8zWxA7Sykk3QxcY2avxM5SKkmXAc+Y2ROxs6wukp43s71j56gPSZsBfwY2N7ODJXUF9jCzWyJHqzdJJwIDgPsIn0tHA5eY2fCV/kVX1rzmPI5cve18SdsDHwId4sUpjqSxhJ0RmxFayH0iaZyZnRU1WHH+CaQ4KJmU/dwL6EroeAJhxmpylETFST0/ucF3tlnSI2b2aPb4YCClmehvgJmSngQW5Q6mcIGU2Rvok10s/ZulbQhTuvv1a+A8Sd8SNvRJqpWipPyOV1XArqS1CdQQYDChJSeE3WXvJnQuSoKZDZM0idDCVYSOS8lesLrAZ84jyPoj30/YenoIsAFwoZndGDNXfeXN/J8CtDOzgSm1IQSQdAvQBRhFgguxJI0hdMxZnD1uDjxhZvvHTVY/qecHkDTZzH64zLFkyhRS3kwMQNKWhY6n1Eoxddn7ODeIWEJor3ulmb0eLVQRJL1sZrvl12hLmmZmO8fO5po2nzmP4+msPuxZsm2OJSVxKzzTTNL3CLfPzl/VL5epOdmf5qTVoSJnc8IMVa6+eYPsWCpSzw+h08MFwG2EAUovIJkyqWwzsXWB9vndl1JhZu9J2hvYxswGS9qE8DpKhkIPvxOAjmb2R0ntgO+Z2cTI0errYKAn4c5vbjxxLGEtVQoWZXXzuW4t3UnzjqqrMD44j+N+wm58+e4jgT7hmUHA48DzZvZytuL9jciZivUo8HvqfqkY6XypXAZMzWauAPYlLHBNRaH8SXSoyHMcMBB4MHv8bHYsCZJ6AFcCawEdJe0MDDKzQ+Mmqx9JAwllFF0IpQnNCRdKe8XMVaTrCQvSDyDsFPol8HfC5lYpeAiYD0whlEml5izgEaCTpPHAJsCRcSM552UtjSrbQXA74HLCrmQ5LYFzzWy7KMGaoGx3wXOAWaS7u2BboFv28CUz+zBmnmKlnj91kiYTBoVj827pJ9MBRdI04AfAlLz8qZXXTTGzXZYpq5huZjvFzlYfSmw3zUIkNSNc4AmYnSu1cy4mnzlvXF2AQwhdHXrkHV8InBolUQlS79Oe+cTMRsQOUSxJ22Y7UebuvPwz+7m5pM3NbEqsbMWQNChrt/Zw9rhK0u1mdkLkaPUmaQTLd55ZQFj0eqOZlftM4hIzW7DM7ogpzdZ8m+1wmitJWD92oBIsztpa5s5hE/ImCxIwQdIOZjYzdpBSZN9lfQmLiw14TtINCbx3XYXzwXkjMrOHgYcl7WFmL8TO0wDDCX3af0xen/aoiYqX6u6CZxMu5ArtAGeEmdAUtJc0wMwuzfrm30u4NZ6Stwm3we/MHh9D6JvfGbgZ6B0pV33NknQ8UC1pG6AfMCFypmLcI+lGoJWkU4GTCf/uKfkboSxqU0mXEEoqLowbadUkzSR83jQDTpL0Nml2zBlGmBy7Lnt8HOH77ahoiZzDy1qikHQ58Cfga2A0sBPwWzO7LWqweqqEPu2VsLtgyrKFcLcTdkbcH3jMzK6Jm6o4kp41s30KHZP0f+VepiZpPcKC7oMIg6rHgT+mMmso6S/AU9TNf6CZ/S5qsCJl5Y7/RTiHp82s7Cc6VtQpJyeV8sBCJUQplRW5yuWD8whyrZok/Rw4HDgTGJPKB4KkiWa2u6RnCbcEPwQmprJlM6RVW5tP0hEre77cZ/6X6YvcHLgRGE/WVziVshwASa8CPzazOdnj9sBoM+sq3z57jcvVay9zLLWa8+Fm1ntVx9yaIWkIcIOZvZg97gb0MbO+UYO5Js/LWuLIte77KXCnmX2+TN1nubtJ0kaE26+PENqXJbVdM/CipK4JbtbQYyXPGVDWg3OWL8eZR9iM6CrSKsuBUGL0vKS3CLOeHYG+We1z2fcKl9SZsCi6A3nfBeV+B0zSaYRJga0k5e9M3IJwoZeSOndXssWJqXTtqgTdgBMlzcketwdezZXtpHSh5yqLz5xHoLBl8+GEspbdCQtER5pZt5X+RbfaZLOenYCUdxd0kWX18tsSXj+vpVISAuH2PXADYWfWmtxxMyvrnVolbQhsBFwK9M97aqGZfV74b5UXSQMIrVzXBb7Ke2oxcJOZDYgSrImplPIcV3l8cB5JNvP8hZnVZLWfLVNpJSdpM+DPwOZmdrCkrsAeZpbMlsep7y6YDVAGArma53GEHtVJbKBRCa8hAEl7svzM87BogYqgAjucusYl6VJCa93OLO18ZWb2bLxUTYekTsBcM/u3pP2AHYFhZjY/bjLX1PngPJLEv9QfI2z6cb6Z7ZTdip2aYg13qiTdT+jRniuf6A3sZGYrrUkvF5XwGpI0nHD3ZRpLZ57NzPrFS7Vqklpn/7Mf8DGhW0h+x6IkZp8rQdZlph+wBeF11B14odxLiypF1it/V8J38eOEMs0uZvbTmLmc85rzCFb0pU5o65SCNmZ2T3ZrFjNbIqlmVX/JrVadzKxn3uOLsy+aVFTCa2hXoKulN8MxmfB5k1vokr8hmgHJLOyuAP0Iu4G+aGb7Z51bUtspN2W12WfPEcBfzew6SVNjh3LOB+dxpPqlnrNI0sYs3TijO2HzFdd4vpa0t5k9DyBpL8IahlRUwmtoFtAW+CB2kGKYWUcIG7AsWyOfbcriGs83ZvaNJCStnW0w1iV2qCZksaTjgBNZuti++Up+37lG4YPzOJL8Us9zFuH2XydJ4wkbsRwZN1KTcxowNKs9h9D1pE/EPMWqhNdQG+AVSROpWxZyaLxIRZkA7FKPY27NmSupFfAQ8KSkecC/ImdqSk4CfgVcYmbvSOoIJLHfiKtsXnMegaQxwM5Akl/qko4i1Oe1A3oS2lFdmFKP6tRlXUKOJJRHtSLMOpuZDYoarAhZnXkXQnnFbDNbHDlSUSTtW+i4mY1r7CzFkNQW+D5hEHI8S8tbWhJ6Pm8bK1tTlr2eNiT0yv82dh7nXDw+cx7HH2IHaKALzezerOPMgYQe1f8gDNJd43gYmE/Y8v79yFmKlnUoOgvY0sxOlbSNpC5mNjJ2tvoq90H4SvwY+AVhEeLVeccXEtr7uQgSfj0lJ9fHfEXPe0tdF5vPnLui5XY/zNqAzTSzO3xHxMYlaZaZbR87R6kk3U1YmHiimW0vaV1Cl4qdI0dbJUnPm9nekhZS9ws+1yu/ZaRoRZHU08zuj53DucaW10r319nP4dnPE4CvUroD6SqTD84bUQV9qY8kzNYeSNjN7mtgopntFDVYEyLpJuA6M5sZO0spJE0ys13zL+okTffXUOOS9DPCLpXfLQT1gYlrKiSNN7O9VnXMucZWFTtAU2Jme2c/W5hZy7w/LVIZmGeOJtSc/yTbrKE1dduxuTVE0sxsy/K9gSmSZkuakXc8Fd9ms+W5bi2dyFt/kQJJvyxw7LIYWUoh6QbgGOB0wgTBUcBKd0x0rsKsL2nv3INs/5H1I+ZxDvCZc+eSUinbTUv6EXAB0BV4AtgL+IWZjY2ZqxjZRkq3mdnt2ePrgXXM7OS4yepH0gwz2zHv5wbAA2Z2UOxszjUGST8EbiUsxIWwjudkb27gYvPBuXOu0WUbcc0klES9DbxkZp/GTVWcbOb/EcKX+8HA52b227ip6k/SS2bWTdKLwBHAZ8AsM9smcjTnGpWkloTxUGp7LbgK5d1anHMxDCaU5vyIsCPlNEnPmtm1cWOtmqTWeQ9PIfSoHg8MktTazD6Pk6xoI7Me21cQuv4Y8L9xIznXeLKWtD2BDkAzKXQV9XUXLjafOXfORSGpmrB1+f6EjUC+TqHHtqR3WH5Bd46Z2VaNHKnBskHKOj5z6JoSSaMJe0RMBmpyx83sqmihnMMH5865CCQ9TVh49QLwHPC8mX0cN1X9SaoC9jCz8bGzlCrrNX820D7Xax5Iqte8cw2RektaV7m8W4tzLoYZwLfA9sCOQK7XeRLMrBa4MnaOBhpM6JCzR/Z4LvCneHGca3QTJO0QO4Rzy/KZc+dcNFmHkJOAc4C2ZrZ25Ej1JuliwkXGA5bgB6n3mndNnaRXgK2BdwgXqrk9R3yHUBeVLwh1zjU6Sb8B/pOwidV7hI4nz0UNVbyzCKU5NZK+JrHNxKiAXvPONdDBsQM4V4gPzp1zMawLXA1MNrMlscOUwsxaxM7QQAOB0UA7SbeT9ZqPmsi5RiCppZl9ASyMncW5QrysxTnnSiTpUGCf7OHYlBZTVkKveedKIWmkmR2S13kp+Y5LrrL44Nw550og6TJCK8jbs0PHEe4E9I+Xqv4kHUDoNf+fZL3mgSR6zTu3OmQXqM8Cz5nZa7HzOJfjg3PnnCuBpBnAzlnnllzf9qkpLSZLtde8c6tDgQvUqYSBul+guqh8cO6ccyXIBuf75XYEzXYOHZvK4Dz1XvPOrQ5+gerKkS8Idc650vwZmCJpLKFmdR9gQNRExZlB6JazPWGXxPmSXjCzr+PGcq5xFLhA3c0vUF058Jlz55wrQVav+gYwD5hDWFD5YdxUxUu517xzDSHpGsIF6r+B8YT6c79AddH54Nw550qQ+oLKAr3mcwvjnokazLlG5heortz44Nw550qUcr2qpHMJA/Jke8071xB+gerKlQ/OnXOuBL6g0rm0+QWqK1e+INQ550rjCyqdS5iZXRE7g3OF+My5c841gNerOuecW5185tw550pQoF71VkJ5i3POOVcyH5w751xp1gWuxutVnXPOrUZe1uKcc84551yZqIodwDnnnHPOORf44Nw555xzzrky4YNz55xzzjnnyoQPzp1zzjnnnCsTPjh3zjnnnHOuTPw/F9kttM9yCbwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b786d52b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot = True)\n",
    "\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar = False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "instant and mnth = 0.997\n",
      "temp and atemp = 0.996\n",
      "season and mnth = 0.831\n",
      "instant and season = 0.827\n",
      "atemp and cnt = 0.775\n",
      "temp and cnt = 0.771\n",
      "weathersit and hum = 0.581\n",
      "season and cnt = 0.542\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.5 # 相关系数大于0.5的视为强相关\n",
    "\n",
    "corr_list = []\n",
    "size = data_train.shape[1]\n",
    "\n",
    "for i in range(0, size):\n",
    "    for j in range(i+1, size - 2):\n",
    "        if data_corr.iloc[i,j] >threshold:\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j])\n",
    "\n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.3f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "温度和体感温度相关性极强，两者跟cnt的相关性也很大；\n",
    "季节和月份相关性较强，季节与cnt相关性较强"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i4EdfJekkX7KTJBXBIEmSimCQJElFMEiSpCIYJElSEQySJKkIBkmSVASDJEkqgkFSx5rN5NgPW59ndOyH8zTm5mk2s9djSVolfLu8OtL6PKPj7Nn3EBOHZtk+toEP/9rlrF8zyPrzhrwEkKQXzRWSOtL6PKOHODA9w3wzOTA9w3s/+zDfbZygccIrdkt68VwhqSPLfZ7RxKFZLtowQrg4krQCXCGpI6f7PKPDsw0/00jSijBI6kjr84xec8rnGX341y7nJ0aG/EwjSSvCl+zUkdbnGa3htrfXGVkzQOP4ArUanDfoZxpJWhkGSR2r1YJ157U/z+g8/+hIWlm+ZCdJKoJBkiQVwSBJkopgkCRJRTBIkqQiVBqkiNgZEY9HxFRE3LTM4++OiMci4uGI+HJEvKLKeSRJ5aosSBExANwCXAlsBcYjYuuSzQ4C9cy8HLgb+FBV80iSylblCukKYCozpzNzDrgTuHrxBpl5f2Y22jcfADZVOI8kqWBVBulC4PCi20fa953O9cC9Fc4jSSpYlW+3X+56Mst+mltEXAvUgV8+zeO7gd0AmzdvXqn5JEkFqXKFdAS4aNHtTcBTSzeKiDcD7wN2Zebx5Z4oM2/NzHpm1jdu3FjJsJKk3qoySBPAJRFxcUQMA9cA+xdvEBHbgI/RitHTFc4iSSpcZUHKzHngRuA+4NvAXZn5aETcHBG72pt9GFgHfDYiHoqI/ad5OknSKheZyx7WKVa9Xs/JyclejyFJ6lxHn1HjlRokSUUwSJKkIhgkSVIRDJIkqQgGSZJUBIMkSSqCQZIkFcEgSZKKYJAkSUUwSJKkIhgkSVIRDJIkqQgGSZJUBIMkSSqCQZIkFcEgSZKKYJAkSUUwSJKkIhgkSVIRDJIkqQgGSZJUBIMkSSqCQZIkFcEgSZKKYJAkSUUwSJKkIhgkSVIRDJIkqQgGSZJUBIMkSSqCQZIkFcEgSZKKYJAkSUUwSJKkIhgkSVIRDJIkqQgGSZJUBIMkSSqCQZIkFcEgSZKKYJAkSUUwSJKkIhgkSVIRDJIkqQgGSZJUBIMkSSqCQZIkFcEgSZKKYJDOoNlMjh2fp5ntr83s9UiStGoN9nqAUjWbycxzc+zZd5CJQ7NsH9vA3vFtjK4dplaLXo8nSauOK6TTaJxYYM++gxyYnmG+mRyYnmHPvoM0Tiz0ejRJWpUM0mmMDA8wcWj2lPsmDs0yMjzQo4kkaXUzSKfRmFtg+9iGU+7bPraBxpwrJEmqgkE6jZGhAfaOb2PHllEGa8GOLaPsHd/GyJArJEmqgic1nEatFoyuHea26+qMDA/QmFtgZGjAExokqSJ9v0I606ndtVqwbs0gtWh/NUaSVJm+DVKzmRz74TwEPPOD47z7Mw9xwx2TzDw35/uNJKkH+jJIJ99jdMMnJ7n0fffye5/7a979lp9h4/o1ntotST3Sl0FqzD3/PUa/+6cP80/f+EpP7ZakHqk0SBGxMyIej4ipiLhpmcfXRMRn2o8/GBFjVc4DrdXRyJrl32P0ygvWeWq3JPVIZUGKiAHgFuBKYCswHhFbl2x2PfDdzHwl8BHgg1XNc1LjxAJPzjSWfY/R4dmGp3ZLUo9UuUK6ApjKzOnMnAPuBK5ess3VwB3t7+8G3hQRlZ7KNjI8wH/8i//NB996+ZL3GL2GC16yxmvVSVKPVPk+pAuBw4tuHwFed7ptMnM+Ip4FRoFnFm8UEbuB3QCbN29+UUM15hb42+8f5w++9Dgf2PWzvPKCdRyebbB2zSAjw74tS5J6pcoV0nLLjKXnU3eyDZl5a2bWM7O+cePGFzXUySswHP3BcX5l7ze49uMPsnbNIOcN+jKdJPVSlUuCI8BFi25vAp46zTZHImIQeCkwS4W8AoMklanKFdIEcElEXBwRw8A1wP4l2+wHrmt//zbgK5lZ+btSvQKDJJWnshVS+5jQjcB9wADwicx8NCJuBiYzcz/wJ8CnImKK1sromqrmkSSVLbqwIFlR9Xo9Jycnez2GJKlzHb0M1ZdXapAklccgSZKKYJAkSUUwSJKkIhgkSVIRDJIkqQgGSZJUBIMkSSqCQZIkFcEgSZKKYJAkSUUwSJKkIhgkSVIRzrmrfUfEUeBvVuCpzmfJR6X3KfeD++Ak90OL+2Hl98EzmbnzbBudc0FaKRExmZn1Xs/Ra+4H98FJ7ocW90Pv9oEv2UmSimCQJElF6Ocg3drrAQrhfnAfnOR+aHE/9Ggf9O0xJElSWfp5hSRJKsiqD1JE7IyIxyNiKiJuWubxNRHxmfbjD0bEWPenrFYH++DdEfFYRDwcEV+OiFf0Ys6qnW0/LNrubRGREbEqz7TqZD9ExK+3/0w8GhGf7vaMVevg78TmiLg/Ig62/15c1Ys5qxYRn4iIpyPikdM8HhGxt72fHo6I11Y6UGau2l/AAPB/gS3AMPAtYOuSbX4T+Gj7+2uAz/R67h7sgzcCI+3v37Xa9kGn+6G93Xrg68ADQL3Xc/foz8MlwEHgJ9q3L+j13D3YB7cC72p/vxU41Ou5K9oXfxd4LfDIaR6/CrgXCOD1wINVzrPaV0hXAFOZOZ2Zc8CdwNVLtrkauKP9/d3AmyIiujhj1c66DzLz/sxstG8+AGzq8ozd0MmfBYDfBz4E/LCbw3VRJ/vhBuCWzPwuQGY+3eUZq9bJPkjgJe3vXwo81cX5uiYzvw7MnmGTq4FPZssDwMsi4uVVzbPag3QhcHjR7SPt+5bdJjPngWeB0a5M1x2d7IPFrqf1f0SrzVn3Q0RsAy7KzHu6OViXdfLn4VLg0oj4ZkQ8EBFnfYf9OaaTffAB4NqIOAJ8Afit7oxWnBf678eLMljVExdiuZXO0tMKO9nmXNbxf19EXAvUgV+udKLeOON+iIga8BHgHd0aqEc6+fMwSOtluzfQWi1/IyIuy8zvVTxbt3SyD8aB2zPzP0TEDuBT7X3QrH68onT138fVvkI6Aly06PYmnr/0/tE2ETFIa3l+piXsuaaTfUBEvBl4H7ArM493abZuOtt+WA9cBnw1Ig7Rer18/yo8saHTvxN/lpknMvMJ4HFagVotOtkH1wN3AWTmAeA8Wtd36zcd/fuxUlZ7kCaASyLi4ogYpnXSwv4l2+wHrmt//zbgK9k+mrdKnHUftF+q+hitGK224wUnnXE/ZOazmXl+Zo5l5hitY2m7MnOyN+NWppO/E5+ndaILEXE+rZfwprs6ZbU62QdPAm8CiIhX0wrS0a5OWYb9wNvbZ9u9Hng2M79T1W+2ql+yy8z5iLgRuI/WmTWfyMxHI+JmYDIz9wN/Qms5PkVrZXRN7yZeeR3ugw8D64DPts/neDIzd/Vs6Ap0uB9WvQ73w33A34+Ix4AF4L2ZOdO7qVdWh/vgPcBtEfHbtF6iescq+x9VACJiH62XZs9vHy97PzAEkJkfpXX87CpgCmgA76x0nlW4jyVJ56DV/pKdJOkcYZAkSUUwSJKkIhgkSVIRDJIkqQgGSVoBEfGXP+bP/WpEbH0Rv+9YRPzjH/fnpZIYJGkFZOYv/Jg/+qu0rib94xoDDJJWBd+HJK2AiDiWmesi4g20Lsz5DK1LEf1P4NrMzIj498AuYB74EvA54B5aF/R9Fngr8PeA3bQ+FmEK+I3MbETE7cD3aV1r8KeAf5GZd0fEA8CrgSeAOzLzI935L5ZW3qq+UoPUI9uAn6V1za9vAr/YvurBPwRe1Y7TyzLzexGxH7gnM+8GiIjvZeZt7e//La1rqv2n9vO+HPgl4FW0LulyN3AT8DuZ+Q+6958nVcOX7KSV91eZeaR9ZeiHaL2s9n1an7H08Yj4R7Quw7KcyyLiGxHx18A/oRW2kz6fmc3MfAz4yerGl3rDIEkrb/HV0heAwfZnbV0B/Cmt40ZfPM3P3g7cmJk/B/wbWhf1XO55V9OHSEqAL9lJXRER62h9TPwX2sd9ptoP/YDWR1+ctB74TkQM0Voh/b+zPPXSn5fOWa6QpO5YD9wTEQ8DXwN+u33/ncB7I+JgRPw08K+BB4H/DvyvDp73YWA+Ir7VvjK1dM7yLDtJUhFcIUmSimCQJElFMEiSpCIYJElSEQySJKkIBkmSVASDJEkqgkGSJBXh/wPa8i2pL2/LlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b7869beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b75d1c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b74391b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b7379f128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b73726898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b737b2e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b7131f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b737bc978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(data_corr, size = 6, x_vars = cols[i], y_vars = cols[j])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出温度和体感温度基本为完全正线性相关，两者与cnt也比较符合正线性相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "instant列为记录号，不考虑对cnt的影响，将其移除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data_train.drop('instant',axis = 1)\n",
    "data_test = data_test.drop('instant',axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "移除object类型的日期特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data_train.drop('dteday',axis = 1)\n",
    "data_test = data_test.drop('dteday',axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "年份原本为二元变量，用于区分数据年份，拆分训练集和测试集后猜测没有其他作用，而且在打印热度图时无法获取年份与其他数据的相关性（因为值为0），移除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop('yr',axis = 1,inplace = True)\n",
    "data_test.drop('yr',axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为气温和体感温度相关性极强（0.996），而且体感温度的方差更小，体感温度与y的相关性也大于气温，所以先尝试移除气温"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data_train.drop('temp',axis = 1)\n",
    "data_test = data_test.drop('temp',axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "风速最大值为0.5，高于四分之三分位数一倍以上，而且直方图和散点图也显示有部分的离群点，考虑只保留1%-99%分位数中间的数据，用分位数替代离群点的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "limit_windspeed_top = np.percentile(data_train.windspeed.values,99)\n",
    "limit_windspeed_bottom = np.percentile(data_train.windspeed.values,1)\n",
    "data_train['windspeed'].loc[data_train['windspeed']>limit_windspeed_top] = limit_windspeed_top\n",
    "data_train['windspeed'].loc[data_train['windspeed']<limit_windspeed_bottom] = limit_windspeed_bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.498630</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.190967</td>\n",
       "      <td>3405.761644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.110946</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.075071</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046603</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.386470</td>\n",
       "      <td>6043.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           season        mnth     holiday     weekday  workingday  weathersit  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     2.498630    6.526027    0.027397    3.008219    0.684932    1.421918   \n",
       "std      1.110946    3.452584    0.163462    2.006155    0.465181    0.571831   \n",
       "min      1.000000    1.000000    0.000000    0.000000    0.000000    1.000000   \n",
       "25%      2.000000    4.000000    0.000000    1.000000    0.000000    1.000000   \n",
       "50%      3.000000    7.000000    0.000000    3.000000    1.000000    1.000000   \n",
       "75%      3.000000   10.000000    0.000000    5.000000    1.000000    2.000000   \n",
       "max      4.000000   12.000000    1.000000    6.000000    1.000000    3.000000   \n",
       "\n",
       "            atemp         hum   windspeed          cnt  \n",
       "count  365.000000  365.000000  365.000000   365.000000  \n",
       "mean     0.466835    0.643665    0.190967  3405.761644  \n",
       "std      0.168836    0.148744    0.075071  1378.753666  \n",
       "min      0.079070    0.000000    0.046603   431.000000  \n",
       "25%      0.321954    0.538333    0.135583  2132.000000  \n",
       "50%      0.472846    0.647500    0.186900  3740.000000  \n",
       "75%      0.612379    0.742083    0.235075  4586.000000  \n",
       "max      0.840896    0.972500    0.386470  6043.000000  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "湿度的四分之一分位数和四分之三分位数距离中位数都很近，说明取值小于四分之一分位数（或大于四分之三分位数）的数据值都很小（大），从直方图和散点图看，取值小于0.2的数据比较像噪声点，考虑用百分之一分位数代替"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "limit_hum_bottom = np.percentile(data_train.hum.values,1)\n",
    "data_train['hum'].loc[data_train['hum']<limit_hum_bottom] = limit_hum_bottom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对y值中的可能噪声点进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:194: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "limit_cnt_top = np.percentile(data_train.cnt.values,99)\n",
    "data_train['cnt'].loc[data_train['cnt']>limit_cnt_top] = limit_cnt_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#想处理一下200-310范围里cnt小于2000的值，但是没找到合适的方法切片。。先搁置了\n",
    "#limit_cnt_bottom_200_310 = 2000\n",
    "#data_temp = data_train['cnt'].loc[(data_train['cnt']<limit_cnt_bottom_200_310)&(data_train['cnt'].shape[0] > 200)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对类别型特征季节和月份进行one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练集\n",
    "data_train['season'] = data_train['season'].astype(str)\n",
    "data_train['mnth'] = data_train['mnth'].astype(str)\n",
    "data_train['weathersit'] = data_train['weathersit'].astype(str)\n",
    "data_train['weekday'] = data_train['weekday'].astype(str)\n",
    "data_train = pd.get_dummies(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试集\n",
    "data_test['season'] = data_test['season'].astype(str)\n",
    "data_test['mnth'] = data_test['mnth'].astype(str)\n",
    "data_test['weathersit'] = data_test['weathersit'].astype(str)\n",
    "data_test['weekday'] = data_test['weekday'].astype(str)\n",
    "data_test = pd.get_dummies(data_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分离x，y数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data_train.drop(['cnt'],axis = 1)\n",
    "y_train = data_train['cnt']\n",
    "\n",
    "X_test = data_test.drop(['cnt'],axis = 1)\n",
    "y_test = data_test['cnt']\n",
    "\n",
    "columns = X_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对数值型特征进行归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  import sys\n",
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.尝试各回归模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1基础ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#生成一个lr实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看在训练集和测试集中的r2 score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on train is 0.8432754717925844\n",
      "The r2 score of LinearRegression on test is -0.6593823242746786\n"
     ]
    }
   ],
   "source": [
    "# 训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))\n",
    "# 测试集\n",
    "print ('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[9628995619203.207]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[9628995619203.137]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>[6715195424163.43]</td>\n",
       "      <td>weekday_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[6715195424163.407]</td>\n",
       "      <td>weekday_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>[15114152780.051025]</td>\n",
       "      <td>weathersit_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>[15114152779.998627]</td>\n",
       "      <td>weathersit_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>[15114152779.755615]</td>\n",
       "      <td>weathersit_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[4687622973.940735]</td>\n",
       "      <td>season_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[4687622973.876221]</td>\n",
       "      <td>season_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[4687622973.795654]</td>\n",
       "      <td>season_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[4687622973.717285]</td>\n",
       "      <td>season_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.4833984375]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.118408203125]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[-0.1845703125]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[-4129299022.7418213]</td>\n",
       "      <td>mnth_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[-4129299022.7929688]</td>\n",
       "      <td>mnth_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[-4129299022.8302574]</td>\n",
       "      <td>mnth_9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-4129299022.883972]</td>\n",
       "      <td>mnth_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[-4129299022.891388]</td>\n",
       "      <td>mnth_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[-4129299022.9153595]</td>\n",
       "      <td>mnth_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[-4129299022.9465485]</td>\n",
       "      <td>mnth_7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-4129299022.9656982]</td>\n",
       "      <td>mnth_11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[-4129299022.9764404]</td>\n",
       "      <td>mnth_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[-4129299022.9939423]</td>\n",
       "      <td>mnth_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[-4129299023.035095]</td>\n",
       "      <td>mnth_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-4129299023.0636597]</td>\n",
       "      <td>mnth_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[-2913800195039.784]</td>\n",
       "      <td>weekday_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>[-2913800195039.784]</td>\n",
       "      <td>weekday_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[-2913800195039.7886]</td>\n",
       "      <td>weekday_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[-2913800195039.7954]</td>\n",
       "      <td>weekday_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[-2913800195039.796]</td>\n",
       "      <td>weekday_4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     coef       columns\n",
       "1     [9628995619203.207]    workingday\n",
       "0     [9628995619203.137]       holiday\n",
       "27     [6715195424163.43]     weekday_6\n",
       "21    [6715195424163.407]     weekday_0\n",
       "28   [15114152780.051025]  weathersit_1\n",
       "29   [15114152779.998627]  weathersit_2\n",
       "30   [15114152779.755615]  weathersit_3\n",
       "8     [4687622973.940735]      season_4\n",
       "7     [4687622973.876221]      season_3\n",
       "6     [4687622973.795654]      season_2\n",
       "5     [4687622973.717285]      season_1\n",
       "2          [0.4833984375]         atemp\n",
       "4       [-0.118408203125]     windspeed\n",
       "3         [-0.1845703125]           hum\n",
       "16  [-4129299022.7418213]        mnth_5\n",
       "17  [-4129299022.7929688]        mnth_6\n",
       "20  [-4129299022.8302574]        mnth_9\n",
       "10   [-4129299022.883972]       mnth_10\n",
       "15   [-4129299022.891388]        mnth_4\n",
       "19  [-4129299022.9153595]        mnth_8\n",
       "18  [-4129299022.9465485]        mnth_7\n",
       "11  [-4129299022.9656982]       mnth_11\n",
       "12  [-4129299022.9764404]       mnth_12\n",
       "14  [-4129299022.9939423]        mnth_3\n",
       "13   [-4129299023.035095]        mnth_2\n",
       "9   [-4129299023.0636597]        mnth_1\n",
       "23   [-2913800195039.784]     weekday_2\n",
       "26   [-2913800195039.784]     weekday_5\n",
       "24  [-2913800195039.7886]     weekday_3\n",
       "22  [-2913800195039.7954]     weekday_1\n",
       "25   [-2913800195039.796]     weekday_4"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类别型特征的权重系数很奇怪，预测结果也不好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is -0.6800437479555985\n",
      "The r2 score of RidgeCV on train is 0.8433925858749439\n"
     ]
    }
   ],
   "source": [
    "#设置超参数（正则参数）范围\n",
    "n_alphas = 20\n",
    "alphas = np.logspace(-1,1,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "# 评估\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b7879f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.5455594781168519\n"
     ]
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.3Lasso回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is -0.6787377669764805\n",
      "The r2 score of LassoCV on train is 0.8389057059471591\n"
     ]
    }
   ],
   "source": [
    "#设置超参数搜索范围\n",
    "#alphas = [ 0.001, 0.01, 0.1, 1, 10,100]\n",
    "n_alphas = 1000\n",
    "eps = 0.01\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "lasso = LassoCV(eps = eps, n_alphas = n_alphas, max_iter = 10000)  \n",
    "#lasso = LassoCV()  \n",
    "\n",
    "#训练\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#预测\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "# 评估\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b788dd080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.0008226888743697062\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses)\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011646</td>\n",
       "      <td>[9628995619203.207]</td>\n",
       "      <td>[0.025521471586417113]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.032727</td>\n",
       "      <td>[9628995619203.137]</td>\n",
       "      <td>[-0.04742813007234035]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.012516</td>\n",
       "      <td>[6715195424163.43]</td>\n",
       "      <td>[0.022849037556219406]</td>\n",
       "      <td>weekday_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.001532</td>\n",
       "      <td>[6715195424163.407]</td>\n",
       "      <td>[-0.000942379070295344]</td>\n",
       "      <td>weekday_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.061683</td>\n",
       "      <td>[15114152780.051025]</td>\n",
       "      <td>[0.11868554705591342]</td>\n",
       "      <td>weathersit_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[15114152779.998627]</td>\n",
       "      <td>[0.061340748974550656]</td>\n",
       "      <td>weathersit_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-0.236972</td>\n",
       "      <td>[15114152779.755615]</td>\n",
       "      <td>[-0.18002629603046602]</td>\n",
       "      <td>weathersit_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.041011</td>\n",
       "      <td>[4687622973.940735]</td>\n",
       "      <td>[0.1018422295693042]</td>\n",
       "      <td>season_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.009006</td>\n",
       "      <td>[4687622973.876221]</td>\n",
       "      <td>[0.04840524785239545]</td>\n",
       "      <td>season_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.038663</td>\n",
       "      <td>[4687622973.795654]</td>\n",
       "      <td>[-0.028770450766460388]</td>\n",
       "      <td>season_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.192374</td>\n",
       "      <td>[4687622973.717285]</td>\n",
       "      <td>[-0.12147702665523809]</td>\n",
       "      <td>season_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.510500</td>\n",
       "      <td>[0.4833984375]</td>\n",
       "      <td>[0.4263804302263477]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.097902</td>\n",
       "      <td>[-0.118408203125]</td>\n",
       "      <td>[-0.11296449143525707]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.133340</td>\n",
       "      <td>[-0.1845703125]</td>\n",
       "      <td>[-0.16284015370695593]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.136703</td>\n",
       "      <td>[-4129299022.7418213]</td>\n",
       "      <td>[0.17110949282440294]</td>\n",
       "      <td>mnth_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.102488</td>\n",
       "      <td>[-4129299022.7929688]</td>\n",
       "      <td>[0.13262626373046033]</td>\n",
       "      <td>mnth_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.088057</td>\n",
       "      <td>[-4129299022.8302574]</td>\n",
       "      <td>[0.09216380990527114]</td>\n",
       "      <td>mnth_9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.065363</td>\n",
       "      <td>[-4129299022.883972]</td>\n",
       "      <td>[0.04081465400367454]</td>\n",
       "      <td>mnth_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-4129299022.891388]</td>\n",
       "      <td>[0.018694854793466067]</td>\n",
       "      <td>mnth_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-4129299022.9153595]</td>\n",
       "      <td>[0.014431386971414188]</td>\n",
       "      <td>mnth_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.011556</td>\n",
       "      <td>[-4129299022.9465485]</td>\n",
       "      <td>[-0.010004883985292952]</td>\n",
       "      <td>mnth_7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-4129299022.9656982]</td>\n",
       "      <td>[-0.04393925254848545]</td>\n",
       "      <td>mnth_11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-4129299022.9764404]</td>\n",
       "      <td>[-0.05934193259007389]</td>\n",
       "      <td>mnth_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.032362</td>\n",
       "      <td>[-4129299022.9939423]</td>\n",
       "      <td>[-0.08186293313427964]</td>\n",
       "      <td>mnth_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.042863</td>\n",
       "      <td>[-4129299023.035095]</td>\n",
       "      <td>[-0.11840841807777767]</td>\n",
       "      <td>mnth_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.072687</td>\n",
       "      <td>[-4129299023.0636597]</td>\n",
       "      <td>[-0.15628304189278167]</td>\n",
       "      <td>mnth_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.000377</td>\n",
       "      <td>[-2913800195039.784]</td>\n",
       "      <td>[0.0005403100598434816]</td>\n",
       "      <td>weekday_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.005381</td>\n",
       "      <td>[-2913800195039.784]</td>\n",
       "      <td>[0.0016799008127020587]</td>\n",
       "      <td>weekday_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-2913800195039.7886]</td>\n",
       "      <td>[-0.00653896196928158]</td>\n",
       "      <td>weekday_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-2913800195039.7954]</td>\n",
       "      <td>[-0.007213106752776947]</td>\n",
       "      <td>weekday_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-2913800195039.796]</td>\n",
       "      <td>[-0.010374800636411023]</td>\n",
       "      <td>weekday_4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coef_lasso                coef_lr               coef_ridge       columns\n",
       "1     0.011646    [9628995619203.207]   [0.025521471586417113]    workingday\n",
       "0    -0.032727    [9628995619203.137]   [-0.04742813007234035]       holiday\n",
       "27    0.012516     [6715195424163.43]   [0.022849037556219406]     weekday_6\n",
       "21   -0.001532    [6715195424163.407]  [-0.000942379070295344]     weekday_0\n",
       "28    0.061683   [15114152780.051025]    [0.11868554705591342]  weathersit_1\n",
       "29   -0.000000   [15114152779.998627]   [0.061340748974550656]  weathersit_2\n",
       "30   -0.236972   [15114152779.755615]   [-0.18002629603046602]  weathersit_3\n",
       "8     0.041011    [4687622973.940735]     [0.1018422295693042]      season_4\n",
       "7     0.009006    [4687622973.876221]    [0.04840524785239545]      season_3\n",
       "6    -0.038663    [4687622973.795654]  [-0.028770450766460388]      season_2\n",
       "5    -0.192374    [4687622973.717285]   [-0.12147702665523809]      season_1\n",
       "2     0.510500         [0.4833984375]     [0.4263804302263477]         atemp\n",
       "4    -0.097902      [-0.118408203125]   [-0.11296449143525707]     windspeed\n",
       "3    -0.133340        [-0.1845703125]   [-0.16284015370695593]           hum\n",
       "16    0.136703  [-4129299022.7418213]    [0.17110949282440294]        mnth_5\n",
       "17    0.102488  [-4129299022.7929688]    [0.13262626373046033]        mnth_6\n",
       "20    0.088057  [-4129299022.8302574]    [0.09216380990527114]        mnth_9\n",
       "10    0.065363   [-4129299022.883972]    [0.04081465400367454]       mnth_10\n",
       "15    0.000000   [-4129299022.891388]   [0.018694854793466067]        mnth_4\n",
       "19    0.000000  [-4129299022.9153595]   [0.014431386971414188]        mnth_8\n",
       "18   -0.011556  [-4129299022.9465485]  [-0.010004883985292952]        mnth_7\n",
       "11   -0.000000  [-4129299022.9656982]   [-0.04393925254848545]       mnth_11\n",
       "12   -0.000000  [-4129299022.9764404]   [-0.05934193259007389]       mnth_12\n",
       "14   -0.032362  [-4129299022.9939423]   [-0.08186293313427964]        mnth_3\n",
       "13   -0.042863   [-4129299023.035095]   [-0.11840841807777767]        mnth_2\n",
       "9    -0.072687  [-4129299023.0636597]   [-0.15628304189278167]        mnth_1\n",
       "23    0.000377   [-2913800195039.784]  [0.0005403100598434816]     weekday_2\n",
       "26    0.005381   [-2913800195039.784]  [0.0016799008127020587]     weekday_5\n",
       "24   -0.000000  [-2913800195039.7886]   [-0.00653896196928158]     weekday_3\n",
       "22   -0.000000  [-2913800195039.7954]  [-0.007213106752776947]     weekday_1\n",
       "25   -0.000000   [-2913800195039.796]  [-0.010374800636411023]     weekday_4"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看各特征的权重系数\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出基本ols的很多权重系数都很大，岭回归用L2正则促使他们都变得比较小，而lasso把很多特征的权重系数都置成了0。可是预测结果都为负数，调整参数也没什么改善，不知道到底是哪里出了问题。。望老师解答"
   ]
  }
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